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Gonzalez R, Saha A, Campbell CJ, Nejat P, Lokker C, Norgan AP. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. J Pathol Inform 2024; 15:100347. [PMID: 38162950 PMCID: PMC10755052 DOI: 10.1016/j.jpi.2023.100347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/06/2023] [Accepted: 11/01/2023] [Indexed: 01/03/2024] Open
Abstract
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Affiliation(s)
- Ricardo Gonzalez
- DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
- Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
| | - Ashirbani Saha
- Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Clinton J.V. Campbell
- William Osler Health System, Brampton, Ontario, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Peyman Nejat
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Cynthia Lokker
- Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Andrew P. Norgan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States
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Georgakopoulou VE, Lempesis IG, Sklapani P, Trakas N, Spandidos DA. Precision medicine for respiratory diseases: A current viewpoint. MEDICINE INTERNATIONAL 2024; 4:31. [PMID: 38680944 PMCID: PMC11046260 DOI: 10.3892/mi.2024.155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 04/11/2024] [Indexed: 05/01/2024]
Abstract
In the realm of respiratory illnesses, despite the immense costs and efforts invested in diagnosis and treatment, numerous patients with chronic respiratory conditions or malignancies do not respond well to existing therapies. Delayed diagnoses and inadequate treatments contribute to these challenges, along with adverse reactions or treatment limitations due to side-effects. However, recent advancements in understanding respiratory diseases have paved the way for personalized medical treatments, considering individual genetic, molecular and environmental factors. Precision medicine, which accommodates individual differences in disease susceptibility and response to treatments, aims to improve patient care by aligning medical research with tailored therapies. Innovative technologies, such as genomic sequencing and biomarker identification contribute to this approach, allowing for customized treatments and the identification of effective therapies. Additionally, the application of precision medicine in lung cancer treatment exemplifies the forefront of individualized care within respiratory medicine. Several studies have explored the role of precision medicine in managing respiratory infectious diseases, asthma and idiopathic pulmonary fibrosis, aiming to categorize diseases more accurately and design targeted therapies. The ultimate goal is to enhance treatment effectiveness, minimize adverse events, and shift towards a patient-centered approach to managing respiratory conditions. Despite limitations, precision medicine holds promise for improving patient outcomes and emphasizing personalized care in respiratory medicine.
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Affiliation(s)
| | - Ioannis G. Lempesis
- Department of Pathophysiology, Laiko General Hospital, Medical School of National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Pagona Sklapani
- Department of Biochemistry, Sismanogleio Hospital, 15126 Athens, Greece
| | - Nikolaos Trakas
- Department of Biochemistry, Sismanogleio Hospital, 15126 Athens, Greece
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Schiffer-Kane K, Liu C, Callahan TJ, Ta C, Nestor JG, Weng C. Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation. J Biomed Inform 2024; 155:104659. [PMID: 38777085 PMCID: PMC11181468 DOI: 10.1016/j.jbi.2024.104659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/11/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study aims to promote interoperability in precision medicine and translational research by aligning the Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models. Phenopackets is an expert knowledge-driven schema designed to facilitate the storage and exchange of multimodal patient data, and support downstream analysis. The first goal of this paper is to explore model alignment by characterizing the common data models using a newly developed data transformation process and evaluation method. Second, using OMOP normalized clinical data, we evaluate the mapping of real-world patient data to Phenopackets. We evaluate the suitability of Phenopackets as a patient data representation for real-world clinical cases. METHODS We identified mappings between OMOP and Phenopackets and applied them to a real patient dataset to assess the transformation's success. We analyzed gaps between the models and identified key considerations for transforming data between them. Further, to improve ambiguous alignment, we incorporated Unified Medical Language System (UMLS) semantic type-based filtering to direct individual concepts to their most appropriate domain and conducted a domain-expert evaluation of the mapping's clinical utility. RESULTS The OMOP to Phenopacket transformation pipeline was executed for 1,000 Alzheimer's disease patients and successfully mapped all required entities. However, due to missing values in OMOP for required Phenopacket attributes, 10.2 % of records were lost. The use of UMLS-semantic type filtering for ambiguous alignment of individual concepts resulted in 96 % agreement with clinical thinking, increased from 68 % when mapping exclusively by domain correspondence. CONCLUSION This study presents a pipeline to transform data from OMOP to Phenopackets. We identified considerations for the transformation to ensure data quality, handling restrictions for successful Phenopacket validation and discrepant data formats. We identified unmappable Phenopacket attributes that focus on specialty use cases, such as genomics or oncology, which OMOP does not currently support. We introduce UMLS semantic type filtering to resolve ambiguous alignment to Phenopacket entities to be most appropriate for real-world interpretation. We provide a systematic approach to align OMOP and Phenopackets schemas. Our work facilitates future use of Phenopackets in clinical applications by addressing key barriers to interoperability when deriving a Phenopacket from real-world patient data.
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Affiliation(s)
- Kayla Schiffer-Kane
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Jordan G Nestor
- Department of Medicine, Division of Nephrology, Columbia University Irving Medical Center, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
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Chen J, Goudey B, Geard N, Verspoor K. Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation. Bioinformatics 2024; 40:i390-i400. [PMID: 38940182 DOI: 10.1093/bioinformatics/btae246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. RESULTS We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies. This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge. AVAILABILITY AND IMPLEMENTATION https://github.com/jiyuc/de-inconsistency.
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Affiliation(s)
- Jiyu Chen
- School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia
- Data61, The Commonwealth Scientific and Industrial Research Organisation, Marsfield 2122, NSW, Australia
| | - Benjamin Goudey
- School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia
| | - Nicholas Geard
- School of Computing and Information Systems, The University of Melbourne, Parkville 3010, VIC, Australia
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Victoria 3000, Australia
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Mullen KR, Tammen I, Matentzoglu NA, Mather M, Mungall CJ, Haendel MA, Nicholas FW, Toro S. The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization. ARXIV 2024:arXiv:2406.02623v1. [PMID: 38883236 PMCID: PMC11177956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Background – Limited universally adopted data standards in veterinary science hinders data interoperability and therefore integration and comparison; this ultimately impedes application of existing information-based tools to support advancement in veterinary diagnostics, treatments, and precision medicine. Hypothesis/Objectives – Creation of a Vertebrate Breed Ontology (VBO) as a single, coherent logic-based standard for documenting breed names in animal health, production and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals – No live animals were used in this study. Methods – A list of breed names and related information was compiled from relevant sources, organizations, communities, and experts using manual and computational approaches to create VBO. Each breed is represented by a VBO term that includes all provenance and the breed's related information as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results – VBO is an open, community-driven ontology representing over 19,000 livestock and companion animal breeds covering 41 species. Breeds are classified based on community and expert conventions (e.g., horse breed, cattle breed). This classification is supported by relations to the breeds' genus and species indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g. relating breeds to their foundation stock, provide additional context to support advanced data analytics. VBO term metadata includes common names and synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance – Veterinary data interoperability and computability can be enhanced by the adoption of VBO as a source of standard breed names in databases and veterinary electronic health records.
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Danis D, Bamshad MJ, Bridges Y, Cacheiro P, Carmody LC, Chong JX, Coleman B, Dalgleish R, Freeman PJ, Graefe ASL, Groza T, Jacobsen JOB, Klocperk A, Kusters M, Ladewig MS, Marcello AJ, Mattina T, Mungall CJ, Munoz-Torres MC, Reese JT, Rehburg F, Reis BCS, Schuetz C, Smedley D, Strauss T, Sundaramurthi JC, Thun S, Wissink K, Wagstaff JF, Zocche D, Haendel MA, Robinson PN. A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24308104. [PMID: 38854034 PMCID: PMC11160806 DOI: 10.1101/2024.05.29.24308104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present phenopacket-store. Version 0.1.12 of phenopacket-store includes 4916 phenopackets representing 277 Mendelian and chromosomal diseases associated with 236 genes, and 2872 unique pathogenic alleles curated from 605 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.
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Affiliation(s)
- Daniel Danis
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael J Bamshad
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA
- Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle WA 98195, USA
- Department of Pediatrics, Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98195, USA
| | - Yasemin Bridges
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Pilar Cacheiro
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Leigh C Carmody
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
| | - Jessica X Chong
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA
- Brotman-Baty Institute for Precision Medicine, 1959 NE Pacific Street, Box 357657, Seattle WA 98195, USA
| | - Ben Coleman
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
| | - Raymond Dalgleish
- Department of Genetics and Genome Biology, University of Leicester, Leicester, UK
| | - Peter J Freeman
- Division of Informatics, Imaging and Data Science, The University of Manchester, Manchester, UK
| | - Adam S L Graefe
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tudor Groza
- Rare Care Centre, Perth Children's Hospital, Nedlands, WA 6009, Australia
- SingHealth Duke-NUS Institute of Precision Medicine, 5 Hospital Drive Level 9, Singapore 169609, Singapore
- Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Julius O B Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Adam Klocperk
- Department of Immunology, 2nd Faculty of Medicine, Charles University and University Hospital in Motol, Prague, Czech Republic
| | - Maaike Kusters
- Department of Paediatric Immunology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- University College London Institute of Child Health, London, United Kingdom
| | - Markus S Ladewig
- Department of Ophthalmology, University Clinic Marburg - Campus Fulda, Fulda, Germany
| | - Anthony J Marcello
- Department of Pediatrics, Division of Genetic Medicine, University of Washington, 1959 NE Pacific Street, Box 357371, Seattle, WA 98195, USA
| | - Teresa Mattina
- Medica Genetics University of Catania Italy
- Morgagni foundation and Clinic, Catania, Italy
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Monica C Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Ccampus
| | - Justin T Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Filip Rehburg
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Bárbara C S Reis
- Department of Immunology, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil
- High Complexity Laboratory, National Institute of Women's, Children's and Adolescents' Health Fernandes Figueira, Rio de Janeiro, Brazil
| | - Catharina Schuetz
- Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Damian Smedley
- William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Timmy Strauss
- Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- University Center for Rare Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | - Sylvia Thun
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kyran Wissink
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Utrecht University, Utrecht, the Netherlands
| | | | - David Zocche
- North West Thames Regional Genetics Service, Northwick Park & St Mark's Hospitals, London, UK
| | | | - Peter N Robinson
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- The Jackson Institute for Genomic Medicine, 10 Discovery Drive, Farmington CT 06032, USA
- ELLIS-European Laboratory for Learning and Intelligent Systems
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Pan B, Ma X, Zhou S, Cheng X, Fang J, Yi Q, Li Y, Li S, Yang J. Predicting mitophagy-related genes and unveiling liver endothelial cell heterogeneity in hepatic ischemia-reperfusion injury. Front Immunol 2024; 15:1370647. [PMID: 38694511 PMCID: PMC11061384 DOI: 10.3389/fimmu.2024.1370647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Background Hepatic Ischemia-Reperfusion Injury (HIRI) is a major complication in liver transplants and surgeries, significantly affecting postoperative outcomes. The role of mitophagy, essential for removing dysfunctional mitochondria and maintaining cellular balance, remains unclear in HIRI. Methods To unravel the role of mitophagy-related genes (MRGs) in HIRI, we assembled a comprehensive dataset comprising 44 HIRI samples alongside 44 normal control samples from the Gene Expression Omnibus (GEO) database for this analysis. Using Random Forests and Support Vector Machines - Recursive Feature Elimination (SVM-RFE), we pinpointed eight pivotal genes and developed a logistic regression model based on these findings. Further, we employed consensus cluster analysis for classifying HIRI patients according to their MRG expression profiles and conducted weighted gene co-expression network analysis (WGCNA) to identify clusters of genes that exhibit high correlation within different modules. Additionally, we conducted single-cell RNA sequencing data analysis to explore insights into the behavior of MRGs within the HIRI. Results We identified eight key genes (FUNDC1, VDAC1, MFN2, PINK1, CSNK2A2, ULK1, UBC, MAP1LC3B) with distinct expressions between HIRI and controls, confirmed by PCR validation. Our diagnostic model, based on these genes, accurately predicted HIRI outcomes. Analysis revealed a strong positive correlation of these genes with monocytic lineage and a negative correlation with B and T cells. HIRI patients were divided into three subclusters based on MRG profiles, with WGCNA uncovering highly correlated gene modules. Single-cell analysis identified two types of endothelial cells with different MRG scores, indicating their varied roles in HIRI. Conclusions Our study highlights the critical role of MRGs in HIRI and the heterogeneity of endothelial cells. We identified the macrophage migration inhibitory factor (MIF) and cGAS-STING (GAS) pathways as regulators of mitophagy's impact on HIRI. These findings advance our understanding of mitophagy in HIRI and set the stage for future research and therapeutic developments.
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Affiliation(s)
- Bochen Pan
- Department of Biochemistry, Zunyi Medical University, Zunyi, Guizhou, China
| | - Xuan Ma
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Shihuan Zhou
- Department of Biochemistry, Zunyi Medical University, Zunyi, Guizhou, China
| | - Xiaoling Cheng
- Department of Cell Biology, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jianwei Fang
- Department of Biochemistry, Zunyi Medical University, Zunyi, Guizhou, China
| | - Qiuyun Yi
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
| | - Yuke Li
- Department of Biochemistry, Zunyi Medical University, Zunyi, Guizhou, China
| | - Song Li
- Department of Biochemistry, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jiawei Yang
- Department of Biochemistry, Zunyi Medical University, Zunyi, Guizhou, China
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Park WY, Jeon K, Schmidt TS, Kondylakis H, Alkasab T, Dewey BE, You SC, Nagy P. Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:899-908. [PMID: 38315345 PMCID: PMC11031512 DOI: 10.1007/s10278-024-00982-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/10/2023] [Accepted: 11/14/2023] [Indexed: 02/07/2024]
Abstract
The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.
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Affiliation(s)
- Woo Yeon Park
- Biomedical Informatics and Data Science, Johns Hopkins University, 855 N Wolfe St, Rangos 616, Baltimore, MD, USA.
| | - Kyulee Jeon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea
| | - Teri Sippel Schmidt
- Biomedical Informatics and Data Science, Johns Hopkins University, 855 N Wolfe St, Rangos 616, Baltimore, MD, USA
| | - Haridimos Kondylakis
- Institute of Computer Science, Foundation of Research & Technology-Hellas (FORTH), Heraklion, Greece
| | - Tarik Alkasab
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Blake E Dewey
- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea
| | - Paul Nagy
- Biomedical Informatics and Data Science, Johns Hopkins University, 855 N Wolfe St, Rangos 616, Baltimore, MD, USA
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Khnaisser C, Looten V, Lavoie L, Burgun A, Ethier JF. Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures. Health Informatics J 2024; 30:14604582241259336. [PMID: 38848696 DOI: 10.1177/14604582241259336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.
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Affiliation(s)
| | - Vincent Looten
- Association des Centres Médicaux et Sociaux (ACMS), Suresnes, France
| | - Luc Lavoie
- Université de Sherbrooke, Département d'informatique, Sherbrooke, QC, Canada
| | - Anita Burgun
- Université de Sherbrooke, Sherbrooke, QC, Canada; Université Paris Cité, Paris, France; Hôpital Européen Georges-Pompidou, Paris, France
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Samitinjay A, Vaishnavi K, Gongireddy R, Kulakarni SC, Panuganti R, Vishwanatham C, Manikanta AK, Biswas R. Understanding clinical complexity in organ and organizational systems: Challenges local and global. J Eval Clin Pract 2024; 30:316-329. [PMID: 37335625 DOI: 10.1111/jep.13886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 04/04/2023] [Accepted: 05/19/2023] [Indexed: 06/21/2023]
Abstract
INTRODUCTION Complexity in healthcare systems has been arbitrarily defined as tasks or systems ranging from complicated to intractable, with a general view of these not being 'simple'. Complexity in healthcare systems in first-world countries has been well elucidated, however, data from third-world countries is still scant. MATERIALS AND METHODS: We present four cases each from three different organ systems-chronic kidney disease, alcohol use disorder, and heart failure-in the backdrop of our healthcare organization. We present our analysis of the complexities faced clinically and, in our local healthcare system which led to these events. RESULTS Analysis of these cases showed that patients with chronic kidney disease had vertebral-spinal pathologies due to poor infection control measures during haemodialysis. All these patients were young with a long history of secondary hypertension. In patients with alcohol use disorder, a common theme of how government regulations and peer pressure promote alcohol use is analysed. In the four patients with unexplained heart failure, vascular health is viewed as a fractal dimension and the various factors affecting vascular health are elaborated. CONCLUSION Complexities exist clinically in making a diagnosis, and organizationally, in the variables and nodes dictating patient outcomes. Clinical complexities cannot be simplified but have to be navigated in an optimized way to improve clinical outcomes.
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Affiliation(s)
- Aditya Samitinjay
- Department of General Medicine, Kamineni Institute of Medical Sciences, Narketpally, India
| | - Karnati Vaishnavi
- Department of General Medicine, Government Medical College, Sangareddy, India
| | | | - Sai Charan Kulakarni
- Department of General Medicine, Kamineni Institute of Medical Sciences, Narketpally, India
| | - Raveen Panuganti
- Department of General Medicine, Kamineni Institute of Medical Sciences, Narketpally, India
| | - Chandana Vishwanatham
- Department of General Medicine, Kamineni Institute of Medical Sciences, Narketpally, India
| | | | - Rakesh Biswas
- Department of General Medicine, Kamineni Institute of Medical Sciences, Narketpally, India
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Fukushima T, Goto K, Hayashi T, Ikeda K, Hatayama T, Yamanaka R, Iwane K, Tasaka R, Kohada Y, Takemoto K, Kobatake K, Goriki A, Toshida A, Nakahara H, Motonaga M, Tokumo K, Fujii Y, Hayes CN, Okamoto W, Kubo T, Matsumoto T, Shiota M, Yamamoto N, Urabe Y, Hiyama E, Arihiro K, Hinoi T, Hinata N. Comprehensive genomic profiling testing in Japanese castration-resistant prostate cancer patients: results of a single-center retrospective cohort study. Jpn J Clin Oncol 2024; 54:175-181. [PMID: 37899139 DOI: 10.1093/jjco/hyad148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023] Open
Abstract
OBJECTIVE Comprehensive genomic profiling testing using a hybrid-capture next-generation sequencing is commonly used in clinical practice to employ precision medicine in cancer treatment worldwide. In this study, we aimed to analyze the profiles obtained using comprehensive genomic profiling testing that was performed in Japanese castration-resistant prostate cancer patients and to discuss the genetic findings in a real-world setting. METHODS A total of 60 cases and 57 castration-resistant prostate cancer patients underwent comprehensive genomic profiling testing between 1 January 2021 and 31 December 2022. Four types of comprehensive genomic profiling testing were selected, and clinically significant cancer-specific gene alterations were identified. RESULTS The median age of patients was 74 years, and the median prostate-specific antigen value at the time of submission was 18.6 ng/ml. Fifty-seven (95%) of 60 cases were metastatic castration-resistant prostate cancers, and 3 cases (5%) were non-metastatic. Among all genetic alterations, androgen-receptor alteration was the most frequently detected in 17 cases (28.3%), followed by 15 cases of TP53 (25.0%), 14 cases of CDK12 (23.3%), 10 cases of phosphatase and tensin homolog (16.7%) and 9 cases of ATM (15.0%) mutations. A total of 13 patients (21.7%) received systemic therapy according to the comprehensive genomic profiling testing results. Overall, the survival rate was significantly greater in the group treated through systemic therapy based on comprehensive genomic profiling testing compared with the group without new therapeutic treatment (P = 0.041). CONCLUSIONS Comprehensive genomic profiling testing is recommended in castration-resistant prostate cancer patients identified as resistant to standard therapy as this can provide a new therapeutic option.
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Affiliation(s)
- Takafumi Fukushima
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Keisuke Goto
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tetsutaro Hayashi
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kenichiro Ikeda
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tomoya Hatayama
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ryoken Yamanaka
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kyosuke Iwane
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Ryo Tasaka
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Kohada
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kenshiro Takemoto
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Kohei Kobatake
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Akihiro Goriki
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Asuka Toshida
- Department of Clinical and Molecular Genetics, Genomic Medicine Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Hikaru Nakahara
- Department of Clinical and Molecular Genetics, Genomic Medicine Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Masanori Motonaga
- Department of Pharmaceutical Services, Hiroshima University Hospital, Hiroshima, Japan
| | - Kentaro Tokumo
- Department of Clinical Oncology, Hiroshima University Hospital, Hiroshima, Japan
| | - Yasutoshi Fujii
- Department of Clinical Oncology, Hiroshima University Hospital, Hiroshima, Japan
- Department of Gastroenterology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - C Nelson Hayes
- Department of Gastroenterology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Wataru Okamoto
- Cancer Treatment Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Toshio Kubo
- Center for Clinical Oncology, Okayama University Hospital, Okayama, Japan
| | - Takashi Matsumoto
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masaki Shiota
- Department of Urology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Noboru Yamamoto
- Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, Japan
| | - Yuji Urabe
- Department of Gastrointestinal Endoscopy and Medicine, Hiroshima University Hospital, Hiroshima, Japan
| | - Eiso Hiyama
- Department of Pediatric Surgery, Hiroshima University Hospital, Hiroshima, Japan
- Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan
| | - Koji Arihiro
- Department of Anatomical Pathology, Hiroshima University Hospital, Hiroshima, Japan
| | - Takao Hinoi
- Department of Clinical and Molecular Genetics, Genomic Medicine Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Nobuyuki Hinata
- Department of Urology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan
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Canfora F, Ottaviani G, Calabria E, Pecoraro G, Leuci S, Coppola N, Sansone M, Rupel K, Biasotto M, Di Lenarda R, Mignogna MD, Adamo D. Advancements in Understanding and Classifying Chronic Orofacial Pain: Key Insights from Biopsychosocial Models and International Classifications (ICHD-3, ICD-11, ICOP). Biomedicines 2023; 11:3266. [PMID: 38137487 PMCID: PMC10741077 DOI: 10.3390/biomedicines11123266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/04/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
In exploring chronic orofacial pain (COFP), this review highlights its global impact on life quality and critiques current diagnostic systems, including the ICD-11, ICOP, and ICHD-3, for their limitations in addressing COFP's complexity. Firstly, this study outlines the global burden of chronic pain and the importance of distinguishing between different pain types for effective treatment. It then delves into the specific challenges of diagnosing COFP, emphasizing the need for a more nuanced approach that incorporates the biopsychosocial model. This review critically examines existing classification systems, highlighting their limitations in fully capturing COFP's multifaceted nature. It advocates for the integration of these systems with the DSM-5's Somatic Symptom Disorder code, proposing a unified, multidisciplinary diagnostic approach. This recommendation aims to improve chronic pain coding standardization and acknowledge the complex interplay of biological, psychological, and social factors in COFP. In conclusion, here, we highlight the need for a comprehensive, universally applicable classification system for COFP. Such a system would enable accurate diagnosis, streamline treatment strategies, and enhance communication among healthcare professionals. This advancement holds potential for significant contributions to research and patient care in this challenging field, offering a broader perspective for scientists across disciplines.
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Affiliation(s)
- Federica Canfora
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
| | - Giulia Ottaviani
- Department of Surgical, Medical and Health Sciences, University of Trieste, 447 Strada di Fiume, 34149 Trieste, Italy
| | - Elena Calabria
- Dentistry Unit, Department of Health Sciences, University of Catanzaro “Magna Graecia”, 88100 Catanzaro, Italy
| | - Giuseppe Pecoraro
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
| | - Stefania Leuci
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
| | - Noemi Coppola
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
| | - Mattia Sansone
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
| | - Katia Rupel
- Department of Surgical, Medical and Health Sciences, University of Trieste, 447 Strada di Fiume, 34149 Trieste, Italy
| | - Matteo Biasotto
- Department of Surgical, Medical and Health Sciences, University of Trieste, 447 Strada di Fiume, 34149 Trieste, Italy
| | - Roberto Di Lenarda
- Department of Surgical, Medical and Health Sciences, University of Trieste, 447 Strada di Fiume, 34149 Trieste, Italy
| | - Michele Davide Mignogna
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
| | - Daniela Adamo
- Department of Neuroscience, Reproductive Sciences and Dentistry, University of Naples Federico II, 5 Via Pansini, 80131 Naples, Italy; (F.C.); (D.A.)
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13
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van Velzen M, de Graaf-Waar HI, Ubert T, van der Willigen RF, Muilwijk L, Schmitt MA, Scheper MC, van Meeteren NLU. 21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework. BMC Med Inform Decis Mak 2023; 23:279. [PMID: 38053104 PMCID: PMC10699040 DOI: 10.1186/s12911-023-02372-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 11/09/2023] [Indexed: 12/07/2023] Open
Abstract
In this paper, we present a framework for developing a Learning Health System (LHS) to provide means to a computerized clinical decision support system for allied healthcare and/or nursing professionals. LHSs are well suited to transform healthcare systems in a mission-oriented approach, and is being adopted by an increasing number of countries. Our theoretical framework provides a blueprint for organizing such a transformation with help of evidence based state of the art methodologies and techniques to eventually optimize personalized health and healthcare. Learning via health information technologies using LHS enables users to learn both individually and collectively, and independent of their location. These developments demand healthcare innovations beyond a disease focused orientation since clinical decision making in allied healthcare and nursing is mainly based on aspects of individuals' functioning, wellbeing and (dis)abilities. Developing LHSs depends heavily on intertwined social and technological innovation, and research and development. Crucial factors may be the transformation of the Internet of Things into the Internet of FAIR data & services. However, Electronic Health Record (EHR) data is in up to 80% unstructured including free text narratives and stored in various inaccessible data warehouses. Enabling the use of data as a driver for learning is challenged by interoperability and reusability.To address technical needs, key enabling technologies are suitable to convert relevant health data into machine actionable data and to develop algorithms for computerized decision support. To enable data conversions, existing classification and terminology systems serve as definition providers for natural language processing through (un)supervised learning.To facilitate clinical reasoning and personalized healthcare using LHSs, the development of personomics and functionomics are useful in allied healthcare and nursing. Developing these omics will be determined via text and data mining. This will focus on the relationships between social, psychological, cultural, behavioral and economic determinants, and human functioning.Furthermore, multiparty collaboration is crucial to develop LHSs, and man-machine interaction studies are required to develop a functional design and prototype. During development, validation and maintenance of the LHS continuous attention for challenges like data-drift, ethical, technical and practical implementation difficulties is required.
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Affiliation(s)
- Mark van Velzen
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands.
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands.
| | - Helen I de Graaf-Waar
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Tanja Ubert
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Robert F van der Willigen
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Lotte Muilwijk
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Institute for Communication, media and information Technology, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Maarten A Schmitt
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
| | - Mark C Scheper
- Data Supported Healthcare: Data-Science unit, Research Center Innovations in care, Rotterdam University of Applied Sciences, Rotterdam, the Netherlands
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Allied Health professions, faculty of medicine and science, Macquarrie University, Sydney, Australia
| | - Nico L U van Meeteren
- Department of Anesthesiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Top Sector Life Sciences and Health (Health~Holland), The Hague, the Netherlands
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14
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Naito T, Noji R, Kugimoto T, Kuroshima T, Tomioka H, Fujiwara S, Suenaga M, Harada H, Kano Y. The Efficacy of Immunotherapy and Clinical Utility of Comprehensive Genomic Profiling in Adenoid Cystic Carcinoma of Head and Neck. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:2111. [PMID: 38138214 PMCID: PMC10745089 DOI: 10.3390/medicina59122111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/24/2023]
Abstract
Background and Objectives: Adenoid cystic carcinoma (ACC) of the head and neck is generally slow-growing but has a high potential for local recurrence and metastasis to distant organs. There is currently no standard pharmacological treatment for recurrent/metastatic (R/M) ACC, and there are cases in which immune checkpoint inhibitors (ICIs) are administered for ACC according to head and neck squamous cell carcinoma (HNSCC). However, the efficacy of ICIs for ACC remains unclear, and the predictive biomarkers need to be elucidated. Materials and Methods: The Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database enabled the retrospective but nationwide analysis of 263 cases of ACC of the head and neck. Then, we examined and reported four cases of ACC that received ICIs and comprehensive genomic profiling (CGP) in our institution. Results: The C-CAT database revealed that 59 cases out of 263 received ICIs, and the best response was 8% of objective response rate (ORR) and 53% of disease control rate (DCR) (complete response, CR 3%, partial response, PR 5%, stable disease, SD 44%, progressive disease, PD 19%, not evaluated, NE 29%). The tumor mutational burden (TMB) in ACC was lower overall compared to HNSCC and could not be useful in predicting the efficacy of ICIs. Some cases with MYB structural variants showed the response to ICIs in the C-CAT database. A patient with MYB fusion/rearrangement variants in our institution showed long-term stable disease. Conclusions: ICI therapy is a potential treatment option, and the MYB structural variant might be a candidate for predictive biomarkers for immunotherapy in patients with R/M ACC.
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Affiliation(s)
- Takahiro Naito
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
- Department of Oral and Maxillofacial Surgical Oncology, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Rika Noji
- Department of Oral and Maxillofacial Surgical Oncology, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Takuma Kugimoto
- Department of Oral and Maxillofacial Surgical Oncology, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Takeshi Kuroshima
- Department of Oral and Maxillofacial Surgical Oncology, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Hirofumi Tomioka
- Department of Oral and Maxillofacial Surgical Oncology, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Shun Fujiwara
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Mitsukuni Suenaga
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Hiroyuki Harada
- Department of Oral and Maxillofacial Surgical Oncology, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Yoshihito Kano
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
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15
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Dam TA, Fleuren LM, Roggeveen LF, Otten M, Biesheuvel L, Jagesar AR, Lalisang RCA, Kullberg RFJ, Hendriks T, Girbes ARJ, Hoogendoorn M, Thoral PJ, Elbers PWG. Augmented intelligence facilitates concept mapping across different electronic health records. Int J Med Inform 2023; 179:105233. [PMID: 37748329 DOI: 10.1016/j.ijmedinf.2023.105233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/15/2023] [Accepted: 09/21/2023] [Indexed: 09/27/2023]
Abstract
INTRODUCTION With the advent of artificial intelligence, the secondary use of routinely collected medical data from electronic healthcare records (EHR) has become increasingly popular. However, different EHR systems typically use different names for the same medical concepts. This obviously hampers scalable model development and subsequent clinical implementation for decision support. Therefore, converting original parameter names to a so-called ontology, a standardized set of predefined concepts, is necessary but time-consuming and labor-intensive. We therefore propose an augmented intelligence approach to facilitate ontology alignment by predicting correct concepts based on parameter names from raw electronic health record data exports. METHODS We used the manually mapped parameter names from the multicenter "Dutch ICU data warehouse against COVID-19" sourced from three types of EHR systems to train machine learning models for concept mapping. Data from 29 intensive care units on 38,824 parameters mapped to 1,679 relevant and unique concepts and 38,069 parameters labeled as irrelevant were used for model development and validation. We used the Natural Language Toolkit (NLTK) to preprocess the parameter names based on WordNet cognitive synonyms transformed by term-frequency inverse document frequency (TF-IDF), yielding numeric features. We then trained linear classifiers using stochastic gradient descent for multi-class prediction. Finally, we fine-tuned these predictions using information on distributions of the data associated with each parameter name through similarity score and skewness comparisons. RESULTS The initial model, trained using data from one hospital organization for each of three EHR systems, scored an overall top 1 precision of 0.744, recall of 0.771, and F1-score of 0.737 on a total of 58,804 parameters. Leave-one-hospital-out analysis returned an average top 1 recall of 0.680 for relevant parameters, which increased to 0.905 for the top 5 predictions. When reducing the training dataset to only include relevant parameters, top 1 recall was 0.811 and top 5 recall was 0.914 for relevant parameters. Performance improvement based on similarity score or skewness comparisons affected at most 5.23% of numeric parameters. CONCLUSION Augmented intelligence is a promising method to improve concept mapping of parameter names from raw electronic health record data exports. We propose a robust method for mapping data across various domains, facilitating the integration of diverse data sources. However, recall is not perfect, and therefore manual validation of mapping remains essential.
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Affiliation(s)
- Tariq A Dam
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands; Pacmed, Amsterdam, the Netherlands.
| | - Lucas M Fleuren
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Luca F Roggeveen
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Martijn Otten
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Laurens Biesheuvel
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Ameet R Jagesar
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | | | | | | | - Armand R J Girbes
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Mark Hoogendoorn
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Patrick J Thoral
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
| | - Paul W G Elbers
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Public Health (APH), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
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16
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Verplaetse N, Passemiers A, Arany A, Moreau Y, Raimondi D. Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease. Genome Biol 2023; 24:224. [PMID: 37798735 PMCID: PMC10552306 DOI: 10.1186/s13059-023-03064-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/20/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Despite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects. RESULTS We demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis. CONCLUSIONS In this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.
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Affiliation(s)
- Nora Verplaetse
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
| | - Antoine Passemiers
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Adam Arany
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Yves Moreau
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Daniele Raimondi
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
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17
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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18
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Agarwal A, Marion J, Nagy P, Robinson M, Walkey A, Sevransky J. How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials. Crit Care Clin 2023; 39:733-749. [PMID: 37704337 DOI: 10.1016/j.ccc.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.
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Affiliation(s)
- Ankita Agarwal
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Emory Critical Care Center, Emory Healthcare, Atlanta, GA, USA
| | | | - Paul Nagy
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew Robinson
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Allan Walkey
- Department of Medicine - Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Jonathan Sevransky
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Emory Critical Care Center, Emory Healthcare, Atlanta, GA, USA.
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19
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Ambalavanan R, Snead RS, Marczika J, Kozinsky K, Aman E. Advancing the Management of Long COVID by Integrating into Health Informatics Domain: Current and Future Perspectives. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6836. [PMID: 37835106 PMCID: PMC10572294 DOI: 10.3390/ijerph20196836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023]
Abstract
The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID's challenges and impact on public health.
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Affiliation(s)
- Radha Ambalavanan
- The Self Research Institute, Broken Arrow, OK 74011, USA; (R.S.S.); (J.M.); (K.K.); (E.A.)
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20
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Batra G, Aktaa S, Camm AJ, Costa F, Di Biase L, Duncker D, Fauchier L, Fragakis N, Frost L, Hijazi Z, Juhlin T, Merino JL, Mont L, Nielsen JC, Oldgren J, Polewczyk A, Potpara T, Sacher F, Sommer P, Tilz R, Maggioni AP, Wallentin L, Casadei B, Gale CP. Data standards for atrial fibrillation/flutter and catheter ablation: the European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart). EUROPEAN HEART JOURNAL. QUALITY OF CARE & CLINICAL OUTCOMES 2023; 9:609-620. [PMID: 36243903 PMCID: PMC10495697 DOI: 10.1093/ehjqcco/qcac068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/02/2022] [Accepted: 10/12/2022] [Indexed: 09/13/2023]
Abstract
AIMS Standardized data definitions are essential for monitoring and assessment of care and outcomes in observational studies and randomized controlled trials (RCTs). The European Unified Registries for Heart Care Evaluation and Randomized Trials (EuroHeart) project of the European Society of Cardiology aimed to develop contemporary data standards for atrial fibrillation/flutter (AF/AFL) and catheter ablation. METHODS AND RESULTS We used the EuroHeart methodology for the development of data standards and formed a Working Group comprising 23 experts in AF/AFL and catheter ablation registries, as well as representatives from the European Heart Rhythm Association and EuroHeart. We conducted a systematic literature review of AF/AFL and catheter ablation registries and data standard documents to generate candidate variables. We used a modified Delphi method to reach a consensus on a final variable set. For each variable, the Working Group developed permissible values and definitions, and agreed as to whether the variable was mandatory (Level 1) or additional (Level 2). In total, 70 Level 1 and 92 Level 2 variables were selected and reviewed by a wider Reference Group of 42 experts from 24 countries. The Level 1 variables were implemented into the EuroHeart IT platform as the basis for continuous registration of individual patient data. CONCLUSION By means of a structured process and working with international stakeholders, harmonized data standards for AF/AFL and catheter ablation for AF/AFL were developed. In the context of the EuroHeart project, this will facilitate country-level quality of care improvement, international observational research, registry-based RCTs, and post-marketing surveillance of devices and pharmacotherapies.
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Affiliation(s)
- Gorav Batra
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, 751 85 Uppsala, Sweden
| | - Suleman Aktaa
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds Institute for Data Analytics, University of Leeds and Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX, UK
| | | | - Francisco Costa
- Cardiology Department, Centro Hospitalar de Lisboa Ocidental EPE Hospital de Santa Cruz, 1449-005 Lisboa, Portugal
| | - Luigi Di Biase
- Division of Cardiology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, New York City, NY 10467, USA
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, 30625 Hannover, Germany
| | - Laurent Fauchier
- Service de Cardiologie, Center Hospitalier Universitaire Trousseau et Faculté de Médecine, Université de Tours, 37044 Tours, France
| | - Nikolaos Fragakis
- 3rd Cardiology Department, Hippokration General Hospital, Aristotle University Medical School, 54124 Thessaloniki, Greece
| | - Lars Frost
- Department of Cardiology, Regional Hospital Central Jutland, Silkeborg, and Department of Clinical Medicine, Aarhus University, 8200 AarhusDenmark
| | - Ziad Hijazi
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, 751 85 Uppsala, Sweden
| | - Tord Juhlin
- Department of Cardiology, Skåne University Hospital, 221 85 Lund, Sweden
| | - José L Merino
- Arrhythmia and Robotic Electrophysiology Unit, Hospital Universitario La Paz, IdiPaz, Universidad Autonoma, 28046 Madrid, Spain
| | - Lluis Mont
- Hospital Clinic, Universitat de Barcelona, Institut de Recerca Biomèdica August Pi Sunyer (IDIBAPS), 08036 Barcelona, Spain; CIBER cardiovascular, 28029 Madrid, Spain
| | - Jens C Nielsen
- Department of Cardiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, 8200 Aarhus, Denmark
| | - Jonas Oldgren
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, 751 85 Uppsala, Sweden
| | - Anna Polewczyk
- Department of Physiology, Patophysiology and Clinical Immunology, Collegium Medicum of The Jan Kochanowski University, 25-369 Kielce, Poland; Department of Cardiac Surgery, Department of Cardiac Surgery Świętokrzyskie Center of Cardiology, Kielce, Poland
| | - Tatjana Potpara
- School of Medicine, University of Belgrade and Intensive Arrhythmia Care, Cardiology Clinic, Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Frederic Sacher
- Electrophysiology and Ablation Unit, Bordeaux University Hospital (CHU), LIRYC Institute, 33600 Bordeaux, France
| | - Philipp Sommer
- Clinic for Electrophysiology, Herz- und Diabeteszentrum NRW, Ruhr-Universität Bochum, 32545 Bad Oeynhausen, Germany
| | - Roland Tilz
- Department of Rhythmology, University Heart Center Luebeck, 23538 Lübeck, Germany
| | - Aldo P Maggioni
- ANMCO Research Center, Heart Care Foundation, 50121 Florence, Italy
| | - Lars Wallentin
- Department of Medical Sciences, Cardiology and Uppsala Clinical Research Center, Uppsala University, 751 85 Uppsala, Sweden
| | - Barbara Casadei
- Division of Cardiovascular Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford OX4 2PG, UK
| | - Chris P Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds Institute for Data Analytics, University of Leeds and Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX, UK
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21
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Calvo-Cidoncha E, Verdinelli J, González-Bueno J, López-Soto A, Camacho Hernando C, Pastor-Duran X, Codina-Jané C, Lozano-Rubí R. An Ontology-Based Approach to Improving Medication Appropriateness in Older Patients: Algorithm Development and Validation Study. JMIR Med Inform 2023; 11:e45850. [PMID: 37477131 PMCID: PMC10366962 DOI: 10.2196/45850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 04/05/2023] Open
Abstract
Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma-an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ≥40, if the Drug Burden Index is ≥1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events.
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Affiliation(s)
| | - Julián Verdinelli
- Clinical Informatics Service, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Javier González-Bueno
- Pharmacy Service, Hospital Dos de Maig, Consorci Sanitari Integral, Barcelona, Spain
| | - Alfonso López-Soto
- Geriatric Unit, Department of Internal Medicine, Hospital Clínic of Barcelona, Barcelona, Spain
| | | | - Xavier Pastor-Duran
- Clinical Informatics Service, Hospital Clínic of Barcelona, Barcelona, Spain
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22
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Matsubara J, Mukai K, Kondo T, Yoshioka M, Kage H, Oda K, Kudo R, Ikeda S, Ebi H, Muro K, Hayashi R, Tokudome N, Yamamoto N, Muto M. First-Line Genomic Profiling in Previously Untreated Advanced Solid Tumors for Identification of Targeted Therapy Opportunities. JAMA Netw Open 2023; 6:e2323336. [PMID: 37459099 DOI: 10.1001/jamanetworkopen.2023.23336] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/20/2023] Open
Abstract
IMPORTANCE Precision oncology using comprehensive genomic profiling (CGP) by next-generation sequencing is aimed at companion diagnosis and genomic profiling. The clinical utility of CGP before the standard of care (SOC) is still not resolved, and more evidence is needed. OBJECTIVE To investigate the clinical utility of next-generation CGP (FoundationOne CDx [F1CDx]) in patients with previously untreated metastatic or recurrent solid tumors. DESIGN, Setting, and Participants This multicenter, prospective, observational cohort study enrolled patients with previously untreated advanced solid tumors between May 18, 2021, and February 16, 2022, with follow-up through August 16, 2022. The study was conducted at 6 hospitals in Japan. Eligible patients were aged 20 years or older and had Eastern Cooperative Oncology Group performance status of 0 to 1 with previously untreated metastatic or recurrent cancers in the gastrointestinal or biliary tract; pancreas, lung, breast, uterus, or ovary; and malignant melanoma. EXPOSURE Comprehensive genomic profiling testing before SOC for advanced solid tumors. MAIN OUTCOMES AND MEASURES Proportion of patients with actionable or druggable genomic alterations and molecular-based recommended therapy (MBRT). RESULTS A total of 183 patients met the inclusion criteria and 180 patients (92 men [51.1%]) with a median age of 64 years (range, 23-88 years) subsequently underwent CGP (lung [n = 28], colon/small intestine [n = 27], pancreas [n = 27], breast [n = 25], biliary tract [n = 20], gastric [n = 19], uterus [n = 12], esophagus [n = 10], ovary [n = 6], and skin melanoma [n = 6]). Data from 172 patients were available for end point analyses. Actionable alterations were found in 172 patients (100.0%; 95% CI, 97.9%-100.0%) and druggable alternations were identified in 109 patients (63.4%; 95% CI, 55.7%-70.6%). The molecular tumor board identified MBRT for 105 patients (61.0%; 95% CI, 53.3%-68.4%). Genomic alterations included in the companion diagnostics list of the CGP test were found in 49 patients (28.5%; 95% CI, 21.9%-35.9%) in a tumor-agnostic setting. After a median follow-up of 7.9 months (range, 0.5-13.2 months), 34 patients (19.8%; 95% CI, 14.1%-26.5%) received MBRT. CONCLUSIONS AND RELEVANCE The findings of this study suggest that CGP testing before SOC for patients with advanced solid tumors may be clinically beneficial to guide the subsequent anticancer therapies, including molecularly matched treatments.
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Affiliation(s)
- Junichi Matsubara
- Department of Clinical Oncology, Kyoto University Hospital, Kyoto, Japan
| | - Kumi Mukai
- Department of Clinical Oncology, Kyoto University Hospital, Kyoto, Japan
| | - Tomohiro Kondo
- Department of Clinical Oncology, Kyoto University Hospital, Kyoto, Japan
| | - Masahiro Yoshioka
- Department of Clinical Oncology, Kyoto University Hospital, Kyoto, Japan
| | - Hidenori Kage
- Department of Clinical Genomics, The University of Tokyo Hospital, Tokyo, Japan
| | - Katsutoshi Oda
- Department of Clinical Genomics, The University of Tokyo Hospital, Tokyo, Japan
| | - Ryo Kudo
- Department of Precision Cancer Medicine, Tokyo Medical and Dental University Hospital, Tokyo, Japan
| | - Sadakatsu Ikeda
- Department of Precision Cancer Medicine, Tokyo Medical and Dental University Hospital, Tokyo, Japan
| | - Hiromichi Ebi
- Division of Molecular Therapeutics, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Kei Muro
- Department of Clinical Oncology, Aichi Cancer Center, Nagoya, Japan
| | - Ryuji Hayashi
- Department of Clinical Oncology, Toyama University Hospital, Toyama, Japan
| | - Nahomi Tokudome
- Internal Medicine III, Wakayama Medical University, Wakayama, Japan
| | | | - Manabu Muto
- Department of Clinical Oncology, Kyoto University Hospital, Kyoto, Japan
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23
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Oommen C, Howlett-Prieto Q, Carrithers MD, Hier DB. Inter-rater agreement for the annotation of neurologic signs and symptoms in electronic health records. Front Digit Health 2023; 5:1075771. [PMID: 37383943 PMCID: PMC10294690 DOI: 10.3389/fdgth.2023.1075771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 05/26/2023] [Indexed: 06/30/2023] Open
Abstract
The extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to signs and symptoms in an ontology. Extracting signs and symptoms from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators.
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Affiliation(s)
- Chelsea Oommen
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Quentin Howlett-Prieto
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Michael D. Carrithers
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
| | - Daniel B. Hier
- Department of Neurology and Rehabilitation, University of Illinois at Chicago, Chicago, IL, United States
- Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, United States
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24
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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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25
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Danis D, Jacobsen JOB, Wagner AH, Groza T, Beckwith MA, Rekerle L, Carmody LC, Reese J, Hegde H, Ladewig MS, Seitz B, Munoz-Torres M, Harris NL, Rambla J, Baudis M, Mungall CJ, Haendel MA, Robinson PN. Phenopacket-tools: Building and validating GA4GH Phenopackets. PLoS One 2023; 18:e0285433. [PMID: 37196000 PMCID: PMC10191354 DOI: 10.1371/journal.pone.0285433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.
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Affiliation(s)
- Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Julius O. B. Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Alex H. Wagner
- Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, United States of America
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States of America
| | | | - Martha A. Beckwith
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Leigh C. Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Justin Reese
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Harshad Hegde
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Markus S. Ladewig
- Department of Ophthalmology, Klinikum Saarbrücken, Saarbrücken, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Monica Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jordi Rambla
- European Genome-Phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Michael Baudis
- University of Zurich and Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Melissa A. Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, United States of America
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Tsakiroglou M, Evans A, Pirmohamed M. Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis. Front Genet 2023; 14:1100352. [PMID: 36968610 PMCID: PMC10036914 DOI: 10.3389/fgene.2023.1100352] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.
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Affiliation(s)
- Maria Tsakiroglou
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- *Correspondence: Maria Tsakiroglou,
| | - Anthony Evans
- Computational Biology Facility, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics. Metabolites 2023; 13:metabo13030373. [PMID: 36984813 PMCID: PMC10058487 DOI: 10.3390/metabo13030373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of the outcome to be explained. Alternatively, this configuration can be represented with a path diagram where the input blocks are each connected by links directed to the outcome—as in multiblock supervised modeling—and are also related to each other, thus allowing one to account for block effects. On the basis of a path model, we show herein how to estimate the effect of an input block, either on its own or conditionally to other(s), on the output response, respectively called “global” and “partial” effects, by percentages of explained variance in dedicated PLS regression models. These effects have been computed in two different path diagrams in a case study relative to metabolic syndrome, involving metabolomics and clinical data from an older men′s cohort (NuAge). From the two effects associated with each path, the results highlighted the complementary information provided by metabolomics to clinical data and, reciprocally, in the metabolic syndrome exploration.
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Kim MH, Park S, Park YR, Ji W, Kim SG, Choo M, Hwang SS, Lee JC, Kim HR, Choi CM. Stratifying non-small cell lung cancer patients using an inverse of the treatment decision rules: validation using electronic health records with application to an administrative database. BMC Med Inform Decis Mak 2023; 23:3. [PMID: 36609301 PMCID: PMC9825000 DOI: 10.1186/s12911-022-02088-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 12/15/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND To validate a stratification method using an inverse of treatment decision rules that can classify non-small cell lung cancer (NSCLC) patients in real-world treatment records. METHODS (1) To validate the index classifier against the TNM 7th edition, we analyzed electronic health records of NSCLC patients diagnosed from 2011 to 2015 in a tertiary referral hospital in Seoul, Korea. Predictive accuracy, stage-specific sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and c-statistic were measured. (2) To apply the index classifier in an administrative database, we analyzed NSCLC patients in Korean National Health Insurance Database, 2002-2013. Differential survival rates among the classes were examined with the log-rank test, and class-specific survival rates were compared with the reference survival rates. RESULTS (1) In the validation study (N = 1375), the overall accuracy was 93.8% (95% CI: 92.5-95.0%). Stage-specific c-statistic was the highest for stage I (0.97, 95% CI: 0.96-0.98) and the lowest for stage III (0.82, 95% CI: 0.77-0.87). (2) In the application study (N = 71,593), the index classifier showed a tendency for differentiating survival probabilities among classes. Compared to the reference TNM survival rates, the index classification under-estimated the survival probability for stages IA, IIIB, and IV, and over-estimated it for stages IIA and IIB. CONCLUSION The inverse of the treatment decision rules has a potential to supplement a routinely collected database with information encoded in the treatment decision rules to classify NSCLC patients. It requires further validation and replication in multiple clinical settings.
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Affiliation(s)
- Min-Hyung Kim
- grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA USA ,grid.31501.360000 0004 0470 5905Department of Preventive Medicine and Family Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Sojung Park
- grid.411076.5Department of Respiratory and Critical Care Medicine, College of Medicine, Ewha Womans University Medical Center, Seoul, Republic of Korea
| | - Yu Rang Park
- grid.15444.300000 0004 0470 5454Department of Biomedical System Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Wonjun Ji
- grid.267370.70000 0004 0533 4667Department of Respiratory and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seul-Gi Kim
- grid.267370.70000 0004 0533 4667Department of Respiratory and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Minji Choo
- grid.267370.70000 0004 0533 4667Department of Respiratory and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea ,grid.31501.360000 0004 0470 5905Department of Epidemiology, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Seung-Sik Hwang
- grid.31501.360000 0004 0470 5905Department of Epidemiology, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jae Cheol Lee
- grid.267370.70000 0004 0533 4667Department of Oncology, College of Medicine, Asan Medical Center, University of Ulsan, Seoul, Republic of Korea
| | - Hyeong Ryul Kim
- grid.267370.70000 0004 0533 4667Department of Thoracic and Cardiovascular Surgery, College of Medicine, Asan Medical Center, University of Ulsan, Seoul, Republic of Korea
| | - Chang-Min Choi
- grid.267370.70000 0004 0533 4667Department of Respiratory and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea ,grid.267370.70000 0004 0533 4667Department of Oncology, College of Medicine, Asan Medical Center, University of Ulsan, Seoul, Republic of Korea ,grid.267370.70000 0004 0533 4667Department of Pulmonary and Critical Care Medicine, Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa–gu, Seoul, 05505 South Korea
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Yu H, Li L, Huffman A, Beverley J, Hur J, Merrell E, Huang HH, Wang Y, Liu Y, Ong E, Cheng L, Zeng T, Zhang J, Li P, Liu Z, Wang Z, Zhang X, Ye X, Handelman SK, Sexton J, Eaton K, Higgins G, Omenn GS, Athey B, Smith B, Chen L, He Y. A new framework for host-pathogen interaction research. Front Immunol 2022; 13:1066733. [PMID: 36591248 PMCID: PMC9797517 DOI: 10.3389/fimmu.2022.1066733] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.
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Affiliation(s)
- Hong Yu
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Li Li
- Department of Genetics, Harvard Medical School, Boston, MA, United States
| | - Anthony Huffman
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - John Beverley
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
- Asymmetric Operations Sector, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Junguk Hur
- Department of Biomedical Sciences, University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, United States
| | - Eric Merrell
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Hsin-hui Huang
- University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yang Wang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Liang Cheng
- Department of Bioinformatics, Harbin Medical University, Harbin, Helongjian, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Jingsong Zhang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Pengpai Li
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhiping Liu
- Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhigang Wang
- Department of Biomedical Engineering, Institute of Basic Medical Sciences and School of Basic Medicine, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Xiangyan Zhang
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | - Xianwei Ye
- Department of Respiratory and Critical Care Medicine, Guizhou Provincial People’s Hospital and National Health Commission (NHC) Key Laboratory of Immunological Diseases, People’s Hospital of Guizhou Province, Guiyang, Guizhou, China
- Department of Basic Medicine, Guizhou University Medical College, Guiyang, Guizhou, China
| | | | - Jonathan Sexton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Kathryn Eaton
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gerry Higgins
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Gilbert S. Omenn
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, United States
| | - Barry Smith
- Department of Philosophy, University at Buffalo, Buffalo, NY, United States
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, United States
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Khnaisser C, Lavoie L, Fraikin B, Barton A, Dussault S, Burgun A, Ethier JF. Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications. Methods Inf Med 2022; 61:e73-e88. [PMID: 35709746 PMCID: PMC9788910 DOI: 10.1055/a-1877-9498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task. OBJECTIVES This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications? METHOD This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing. RESULTS This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents O n t o R e l a , a prototype developed to demonstrate the conversion rules. CONCLUSION This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.
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Affiliation(s)
- Christina Khnaisser
- GRIIS, Université de Sherbrooke, Sherbrooke, Canada,Address for correspondence Christina Khnaisser, PhD, GRIIS Université de SherbrookeSherbrooke J1K 2R1Canada
| | - Luc Lavoie
- GRIIS, Université de Sherbrooke, Sherbrooke, Canada
| | | | | | | | - Anita Burgun
- INSERM UMRS 1138 Team 22, Université de Paris, Paris, France
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Kazemi-Harikandei SZ, Shobeiri P, Salmani Jelodar MR, Tavangar SM. Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review. NEUROSCIENCE INFORMATICS 2022; 2:100104. [DOI: 10.1016/j.neuri.2022.100104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
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van der Wouden CH, Guchelaar HJ, Swen JJ. Precision Medicine Using Pharmacogenomic Panel-Testing: Current Status and Future Perspectives. Clin Lab Med 2022; 42:587-602. [PMID: 36368784 DOI: 10.1016/j.cll.2022.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Cathelijne H van der Wouden
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands; Leiden Network for Personalised Therapeutics, Leiden, The Netherlands
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands; Leiden Network for Personalised Therapeutics, Leiden, The Netherlands
| | - Jesse J Swen
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333ZA, The Netherlands; Leiden Network for Personalised Therapeutics, Leiden, The Netherlands.
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Moris D, Henao R, Hensman H, Stempora L, Chasse S, Schobel S, Dente CJ, Kirk AD, Elster E. Multidimensional machine learning models predicting outcomes after trauma. Surgery 2022; 172:1851-1859. [PMID: 36116976 DOI: 10.1016/j.surg.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.
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Affiliation(s)
| | | | - Hannah Hensman
- DecisionQ, Arlington, VA; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Linda Stempora
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Scott Chasse
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Seth Schobel
- Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD
| | | | - Allan D Kirk
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Eric Elster
- Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Walter Reed National Military Medical Center, Bethesda, MD
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You SC, Lee S, Choi B, Park RW. Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research. Korean Circ J 2022; 52:853-864. [PMID: 36478647 PMCID: PMC9742390 DOI: 10.4070/kcj.2022.0294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 11/10/2022] [Indexed: 08/21/2023] Open
Abstract
A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations. However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world's population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.
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Affiliation(s)
- Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, Korea
| | - Seongwon Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Byungjin Choi
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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Dapas M, Dunaif A. Deconstructing a Syndrome: Genomic Insights Into PCOS Causal Mechanisms and Classification. Endocr Rev 2022; 43:927-965. [PMID: 35026001 PMCID: PMC9695127 DOI: 10.1210/endrev/bnac001] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Indexed: 01/16/2023]
Abstract
Polycystic ovary syndrome (PCOS) is among the most common disorders in women of reproductive age, affecting up to 15% worldwide, depending on the diagnostic criteria. PCOS is characterized by a constellation of interrelated reproductive abnormalities, including disordered gonadotropin secretion, increased androgen production, chronic anovulation, and polycystic ovarian morphology. It is frequently associated with insulin resistance and obesity. These reproductive and metabolic derangements cause major morbidities across the lifespan, including anovulatory infertility and type 2 diabetes (T2D). Despite decades of investigative effort, the etiology of PCOS remains unknown. Familial clustering of PCOS cases has indicated a genetic contribution to PCOS. There are rare Mendelian forms of PCOS associated with extreme phenotypes, but PCOS typically follows a non-Mendelian pattern of inheritance consistent with a complex genetic architecture, analogous to T2D and obesity, that reflects the interaction of susceptibility genes and environmental factors. Genomic studies of PCOS have provided important insights into disease pathways and have indicated that current diagnostic criteria do not capture underlying differences in biology associated with different forms of PCOS. We provide a state-of-the-science review of genetic analyses of PCOS, including an overview of genomic methodologies aimed at a general audience of non-geneticists and clinicians. Applications in PCOS will be discussed, including strengths and limitations of each study. The contributions of environmental factors, including developmental origins, will be reviewed. Insights into the pathogenesis and genetic architecture of PCOS will be summarized. Future directions for PCOS genetic studies will be outlined.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Andrea Dunaif
- Division of Endocrinology, Diabetes and Bone Disease, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records. Sci Rep 2022; 12:19430. [PMID: 36371527 PMCID: PMC9653502 DOI: 10.1038/s41598-022-23101-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathology, hippocampal sclerosis, malformation of cortical dysplasia, tumor, encephalitis, and gliosis. We modeled the diagnosis terms together with their microscopy, immunohistochemistry, anatomy, etiologies, and imaging findings using the description logic-based Web Ontology Language (OWL) in the Epilepsy and Seizure Ontology (EpSO). Three tree-based machine learning models were used to classify the neuropathology reports into one or more diagnosis classes with and without ontology-based feature engineering. We used five-fold cross validation to avoid overfitting with a fixed number of repetitions while leaving out one subset of data for testing, and we used recall, balanced accuracy, and hamming loss as performance metrics for the multi-label classification task. The epilepsy ontology-based feature engineering approach improved the performance of all the three learning models with an improvement of 35.7%, 54.5%, and 33.3% in logistics regression, random forest, and gradient tree boosting models respectively. The run time performance of all three models improved significantly with ontology-based feature engineering with gradient tree boosting model showing a 93.8% reduction in the time required for training and testing of the model. Although, all three models showed an overall improved performance across the three-performance metrics using ontology-based feature engineering, the rate of improvement was not consistent across all input features. To analyze this variation in performance, we computed feature importance scores and found that microscopy had the highest importance score across the three models, followed by imaging, immunohistochemistry, and anatomy in a decreasing order of importance scores. This study showed that ontologies have an important role in feature engineering to make heterogeneous clinical data accessible to machine learning models and also improve the performance of machine learning models in multilabel multiclass classification tasks.
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Dong X, Xiao T, Chen B, Lu Y, Zhou W. Precision medicine via the integration of phenotype-genotype information in neonatal genome project. FUNDAMENTAL RESEARCH 2022; 2:873-884. [PMID: 38933389 PMCID: PMC11197532 DOI: 10.1016/j.fmre.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/07/2022] [Accepted: 07/10/2022] [Indexed: 11/21/2022] Open
Abstract
The explosion of next-generation sequencing (NGS) has enabled the widespread use of genomic data in precision medicine. Currently, several neonatal genome projects have emerged to explore the advantages of NGS to diagnose or screen for rare genetic disorders. These projects have made remarkable achievements, but still the genome data could be further explored with the assistance of phenotype collection. In contrast, longitudinal birth cohorts are great examples to record and apply phenotypic information in clinical studies starting at the neonatal period, especially the trajectory analyses for health development or disease progression. It is obvious that efficient integration of genotype and phenotype benefits not only the clinical management of rare genetic disorders but also the risk assessment of complex diseases. Here, we first summarize the recent neonatal genome projects as well as some longitudinal birth cohorts. Then, we propose two simplified strategies by integrating genotypic and phenotypic information in precision medicine based on current studies. Finally, research collaborations, sociological issues, and future perspectives are discussed. How to maximize neonatal genomic information to benefit the pediatric population remains an area in need of more research and effort.
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Affiliation(s)
- Xinran Dong
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - Tiantian Xiao
- Division of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
- Department of Neonatology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610066, China
| | - Bin Chen
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - Yulan Lu
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - Wenhao Zhou
- Center for Molecular Medicine, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
- Division of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
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Rezaei Z, Vahidnia MH. Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering. GEOJOURNAL 2022; 88:2721-2735. [PMID: 36320661 PMCID: PMC9612622 DOI: 10.1007/s10708-022-10777-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/14/2022] [Indexed: 06/02/2023]
Abstract
The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.
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Affiliation(s)
- Zahra Rezaei
- Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad H. Vahidnia
- Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
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[Rare-disease data standards]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2022; 65:1126-1132. [PMID: 36149471 DOI: 10.1007/s00103-022-03591-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/01/2022] [Indexed: 11/02/2022]
Abstract
The use of standardized data formats (data standards) in healthcare supports four main goals: (1) exchange of data, (2) integration of computer systems and tools, (3) data storage and archiving, and (4) support of federated databases. Standards are especially important for rare-disease research and clinical care.In this review, we introduce healthcare standards and present a selection of standards that are commonly used in the field of rare diseases. The Human Phenotype Ontology (HPO) is the most commonly used standard for annotating phenotypic abnormalities and supporting phenotype-driven analysis of diagnostic exome and genome sequencing. Numerous standards for diseases are available that support a range of needs. Online Mendelian Inheritance in Man (OMIM) and the Orphanet Rare Disease Ontology (ORDO) are the most important standards developed specifically for rare diseases. The Mondo Disease Ontology (Mondo) is a new disease ontology that aims to integrate data from a comprehensive range of current nosologies. New standards and schemas such as the Medical Action Ontology (MAxO) and the Global Alliance for Genomics and Health (GA4GH) phenopacket are being introduced to extend the scope of standards that support rare disease research.In order to provide optimal care for patients with SE in different healthcare settings, it will be necessary to better integrate standards for rare disease with electronic healthcare resources such as the Fast Healthcare Interoperability Resources (FHIR) standard for healthcare data exchange.
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Developing a classification of hematologic neoplasms in the era of precision medicine. Blood 2022; 140:1193-1199. [PMID: 35834418 DOI: 10.1182/blood.2022015849] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/22/2022] [Indexed: 11/20/2022] Open
Abstract
The recently developed International Consensus (IC) classification of hematologic neoplasms is primarily based on input from clinical advisory committees composed of pathologists, hematologists, oncologists, and genomic scientists. Morphology continues to represent a fundamental element in the definition of hematologic neoplasms. Acknowledging that the abnormal morphology is a result of dysregulated hematopoiesis driven by somatic gene mutations or altered expression, the IC classification considers genomic features more extensively. Defining nosologic entities based on underlying molecular mechanism(s) of disease is fundamental for enabling the development of precision treatments. Because translational and clinical research continuously advance the field, the classification of hematologic neoplasms will need to be regularly refined and updated; the basic question is what mechanism should be used for this purpose. Scientific hematopathology societies, in collaboration with hematology societies, should be primarily responsible for establishing a standing International Working Group, which would in turn collaborate with the World Health Organization (WHO)/International Agency for Research on Cancer (IARC) to realize and disseminate the classification. The current classification, with its strong morphology component, represents a basis for refinement. Through data sharing, the creation of large comprehensive patient data sets will allow the use of methods of inference, including statistical analyses and machine learning models, aimed at further identifying distinct disease subgroups. A collaborative clinico-pathologic review process will provide a mechanism for updating pathologic and genomic criteria within a clinical context. An interactive Web-based portal would make the classification more immediately available to the scientific community, while providing accessory features that enable the practical application of diagnostic, prognostic, and predictive information.
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Calvo-Cidoncha E, Camacho-Hernando C, Feu F, Pastor-Duran X, Codina-Jané C, Lozano-Rubí R. OntoPharma: ontology based clinical decision support system to reduce medication prescribing errors. BMC Med Inform Decis Mak 2022; 22:238. [PMID: 36088328 PMCID: PMC9463735 DOI: 10.1186/s12911-022-01979-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/25/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Clinical decision support systems (CDSS) have been shown to reduce medication errors. However, they are underused because of different challenges. One approach to improve CDSS is to use ontologies instead of relational databases. The primary aim was to design and develop OntoPharma, an ontology based CDSS to reduce medication prescribing errors. Secondary aim was to implement OntoPharma in a hospital setting.
Methods
A four-step process was proposed. (1) Defining the ontology domain. The ontology scope was the medication domain. An advisory board selected four use cases: maximum dosage alert, drug-drug interaction checker, renal failure adjustment, and drug allergy checker. (2) Implementing the ontology in a formal representation. The implementation was conducted by Medical Informatics specialists and Clinical Pharmacists using Protégé-OWL. (3) Developing an ontology-driven alert module. Computerised Physician Order Entry (CPOE) integration was performed through a REST API. SPARQL was used to query ontologies. (4) Implementing OntoPharma in a hospital setting. Alerts generated between July 2020/ November 2021 were analysed.
Results
The three ontologies developed included 34,938 classes, 16,672 individuals and 82 properties. The domains addressed by ontologies were identification data of medicinal products, appropriateness drug data, and local concepts from CPOE. When a medication prescribing error is identified an alert is shown. OntoPharma generated 823 alerts in 1046 patients. 401 (48.7%) of them were accepted.
Conclusions
OntoPharma is an ontology based CDSS implemented in clinical practice which generates alerts when a prescribing medication error is identified. To gain user acceptance OntoPharma has been designed and developed by a multidisciplinary team. Compared to CDSS based on relational databases, OntoPharma represents medication knowledge in a more intuitive, extensible and maintainable manner.
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Larsen RR, Maschião LF, Piedade VL, Messas G, Hastings J. More phenomenology in psychiatry? Applied ontology as a method towards integration. Lancet Psychiatry 2022; 9:751-758. [PMID: 35817066 DOI: 10.1016/s2215-0366(22)00156-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/25/2022] [Accepted: 04/22/2022] [Indexed: 12/21/2022]
Abstract
There have been renewed calls to use phenomenology in psychiatry to improve knowledge about causation, diagnostics, and treatment of mental health conditions. A phenomenological approach aims to elucidate the subjective experiences of mental health, which its advocates claim have been largely neglected by current diagnostic frameworks in psychiatry (eg, DSM-5). The consequence of neglecting rich phenomenological information is a comparatively more constrained approach to theory development, empirical research, and care programmes. Although calls for more phenomenology in psychiatry have been met with enthusiasm, there is still relatively little information on how to practically facilitate this integration. In this Personal View, we argue that phenomenological approaches need a shared semantic framework to drive their innovative potential, thus enabling consistent data capture, exchange, and interoperability with current mental health data and informatics approaches (eg, the Research Domain Criteria project). We show how an applied ontology of phenomenological psychopathology offers a suitable method to address these challenges.
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Affiliation(s)
- Rasmus R Larsen
- Department of Philosophy, University of Toronto Mississauga, Mississauga, ON, Canada; Forensic Science Program, University of Toronto Mississauga, Mississauga, ON, Canada.
| | - Luca F Maschião
- Mental Health Department, Santa Casa de São Paulo School of Medical Sciences, São Paulo, Brazil
| | - Valter L Piedade
- Mental Health Department, Santa Casa de São Paulo School of Medical Sciences, São Paulo, Brazil
| | - Guilherme Messas
- Mental Health Department, Santa Casa de São Paulo School of Medical Sciences, São Paulo, Brazil
| | - Janna Hastings
- Department of Clinical, Educational, and Health Psychology, University College London, London, UK; Institute for Intelligent Interacting Systems, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
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43
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Zucco AG, Agius R, Svanberg R, Moestrup KS, Marandi RZ, MacPherson CR, Lundgren J, Ostrowski SR, Niemann CU. Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning. Sci Rep 2022; 12:13879. [PMID: 35974050 PMCID: PMC9380679 DOI: 10.1038/s41598-022-17953-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 08/03/2022] [Indexed: 01/08/2023] Open
Abstract
Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.
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Affiliation(s)
- Adrian G Zucco
- PERSIMUNE Center of Excellence, Rigshospitalet, Copenhagen, Denmark.
| | - Rudi Agius
- Department of Hematology, Rigshospitalet, Copenhagen, Denmark
| | | | | | - Ramtin Z Marandi
- PERSIMUNE Center of Excellence, Rigshospitalet, Copenhagen, Denmark
| | | | - Jens Lundgren
- PERSIMUNE Center of Excellence, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Carsten U Niemann
- Department of Hematology, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Updated Research Priorities of the Society for Vascular Surgery. J Vasc Surg 2022; 76:1432-1439.e2. [DOI: 10.1016/j.jvs.2022.07.176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 07/16/2022] [Accepted: 07/23/2022] [Indexed: 11/19/2022]
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Yang DD, Rio M, Michot C, Boddaert N, Yacoub W, Garcelon N, Thierry B, Bonnet D, Rondeau S, Herve D, Guey S, Angoulvant F, Cormier-Daire V. Natural history of Myhre syndrome. Orphanet J Rare Dis 2022; 17:304. [PMID: 35907855 PMCID: PMC9338657 DOI: 10.1186/s13023-022-02447-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022] Open
Abstract
Background Myhre syndrome (MS) is a rare genetic disease characterized by skeletal disorders, facial features and joint limitation, caused by a gain of function mutation in SMAD4 gene. The natural history of MS remains incompletely understood.
Methods We recruited in a longitudinal retrospective study patients with molecular confirmed MS from the French reference center for rare skeletal dysplasia. We described natural history by chaining data from medical reports, clinical data warehouse, medical imaging and photographies.
Results We included 12 patients. The median age was 22 years old (y/o). Intrauterine and postnatal growth retardation were consistently reported. In preschool age, neurodevelopment disorders were reported in 80% of children. Specifics facial and skeletal features, thickened skin and joint limitation occured mainly in school age children. The adolescence was marked by the occurrence of pulmonary arterial hypertension (PAH) and vascular stenosis. We reported for the first time recurrent strokes from the age of 26 y/o, caused by a moyamoya syndrome in one patient. Two patients died at late adolescence and in their 20 s respectively from PAH crises and mesenteric ischemia. Conclusion Myhre syndrome is a progressive disease with severe multisystemic impairement and life-threathning complication requiring multidisciplinary monitoring.
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Affiliation(s)
- David Dawei Yang
- Centre de Recherche Des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, 75006, Paris, France.,Pediatric Emergency Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France
| | - Marlene Rio
- Université de Paris, Institut IMAGINE, Developmental Brain Disorders Laboratory, INSERM UMR1163, 75015, Paris, France.,Departement of Medical Genetics, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France
| | - Caroline Michot
- Departement of Medical Genetics, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.,Université de Paris, Institut IMAGINE, Molecular and Physiopathological Bases of Osteochondrodysplasia, INSERM UMR1163, 75015, Paris, France
| | - Nathalie Boddaert
- Paediatric Radiology Department, AP-HP, Hôpital Universitaire Necker Enfants Malades, 75015, Paris, France.,Université de Paris, Institut IMAGINE, INSERM1163, 75015, Paris, France
| | - Wael Yacoub
- Paediatric Radiology Department, AP-HP, Hôpital Universitaire Necker Enfants Malades, 75015, Paris, France.,Université de Paris, Institut IMAGINE, INSERM1163, 75015, Paris, France
| | - Nicolas Garcelon
- Centre de Recherche Des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, 75006, Paris, France.,Université de Paris, Institut IMAGINE, Data Science Platform, INSERM UMR1163, 75015, Paris, France
| | - Briac Thierry
- Department of Pediatric Otolaryngology-Head and Neck Surgery, AP-HP, Hôpital Universitaire Necker - Enfants Malades, 75015, Paris, France.,Université de Paris, Human Immunology, Pathophysiology, Immunotherapy/HIPI/INSERM UMR976, Stem Cell Biotechnologies, 75010, Paris, France
| | - Damien Bonnet
- Université de Paris, Institut IMAGINE, INSERM1163, 75015, Paris, France.,M3C-Paediatric Cardiology, AP-HP, Hôpital Universitaire Necker Enfants Malades, 75015, Paris, France
| | - Sophie Rondeau
- Departement of Medical Genetics, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.,Université de Paris, Institut IMAGINE, Molecular and Physiopathological Bases of Osteochondrodysplasia, INSERM UMR1163, 75015, Paris, France
| | - Dominique Herve
- Department of Neurology, AP-HP Nord, Referral Center for Rare Vascular Diseases of the Brain and Retina (CERVCO), DHU NeuroVasc, INSERM U 1161, 75010, Paris, France
| | - Stephanie Guey
- Department of Neurology, AP-HP, Hôpital Lariboisière, UMR-S1161, 75010, Paris, France
| | - Francois Angoulvant
- Centre de Recherche Des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, 75006, Paris, France.,Pediatric Emergency Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France
| | - Valerie Cormier-Daire
- Departement of Medical Genetics, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France. .,Université de Paris, Institut IMAGINE, Molecular and Physiopathological Bases of Osteochondrodysplasia, INSERM UMR1163, 75015, Paris, France.
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Patient safety classification, taxonomy and ontology systems: A systematic review on development and evaluation methodologies. J Biomed Inform 2022; 133:104150. [PMID: 35878822 DOI: 10.1016/j.jbi.2022.104150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/11/2022] [Accepted: 07/19/2022] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Patient safety classifications/ontologies enable patient safety information systems to receive and analyze patient safety data to improve patient safety. Patient safety classifications/ontologies have been developed and evaluated using a variety of methods. The purpose of this review was to discuss and analyze the methodologies for developing and evaluating patient safety classifications/ontologies. METHODS Studies that developed or evaluated patient safety classifications, terminologies, taxonomies, or ontologies were searched through Google Scholar, Google search engines, National Center for Biomedical Ontology (NCBO) BioPortal, Open Biological and Biomedical Ontology (OBO) Foundry and World Health Organization (WHO) websites and Scopus, Web of Science, PubMed, and Science Direct. We updated our search on 30 February 2021 and included all studies published until the end of 2020. Studies that developed or evaluated classifications only for patient safety and provided information on how they were developed or evaluated were included. Systems with covered patient safety terms (such as ICD-10) but are not specifically developed for patient safety were excluded. The quality and the risk of bias of studies were not assessed because all methodologies and criteria were intended to be covered. In addition, we analyzed the data through descriptive narrative synthesis and compared and classified the development and evaluation methods and evaluation criteria according to available development and evaluation approaches for biomedical ontologies. RESULTS We identified 84 articles that met all of the inclusion criteria, resulting in 70 classifications/ontologies, nine of which were for the general medical domain. The most papers were published in 2010 and 2011, with 8 and 7 papers, respectively. The United States (50) and Australia (23) have the most studies. The most commonly used methods for developing classifications/ontologies included the use of existing systems (for expanding or mapping) (44) and qualitative analysis of event reports (39). The most common evaluation methods were coding or classifying some safety report samples (25), quantitative analysis of incidents based on the developed classification (24), and consensus among physicians (16). The most commonly applied evaluation criteria were reliability (27), content and face validity (9), comprehensiveness (6), usability (5), linguistic clarity (5), and impact (4), respectively. CONCLUSIONS Because of the weaknesses and strengths of the development/evaluation methods, it is advised that more than one method for development or evaluation, as well as evaluation criteria, should be used. To organize the processes of developing classification/ontologies, well-established approaches such as Methontology are recommended. The most prevalent evaluation methods applied in this domain are well fitted to the biomedical ontology evaluation methods, but it is also advised to apply some evaluation approaches such as logic, rules, and Natural language processing (NLP) based in combination with other evaluation approaches. This research can assist domain researchers in developing or evaluating domain ontologies using more complete methodologies. There is also a lack of reporting consistency in the literature and same methods or criteria were reported with different terminologies.
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Brakefield WS, Ammar N, Shaban-Nejad A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. JMIR Form Res 2022; 6:e36055. [PMID: 35857363 PMCID: PMC9350817 DOI: 10.2196/36055] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 05/03/2022] [Accepted: 06/07/2022] [Indexed: 01/16/2023] Open
Abstract
Background Many researchers have aimed to develop chronic health surveillance systems to assist in public health decision-making. Several digital health solutions created lack the ability to explain their decisions and actions to human users. Objective This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios. Methods The system design includes a knowledge graph generation component that provides contextual knowledge from relevant domains of interest. This system leverages semantics using concepts, properties, and axioms from existing ontologies. In addition, we used the publicly available US Centers for Disease Control and Prevention 500 Cities data set to perform multivariate analysis. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. Results In this study, we present 2 clinical case scenarios and a proof-of-concept prototype design of a dashboard that provides warnings, recommendations, and explanations and demonstrates the use of UPHO in the context of obesity surveillance, treatment, and prevention. While exploring the case scenarios using a support vector regression machine learning model, we found that poverty, lack of physical activity, education, and unemployment were the most important predictive variables that contribute to obesity in Memphis, TN. Conclusions The application of UPHO could help reduce health disparities and improve urban population health. The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels. International Registered Report Identifier (IRRID) RR2-10.2196/28269
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Affiliation(s)
- Whitney S Brakefield
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States.,Bredesen Center for Data Science, University of Tennessee, Knoxville, TN, United States
| | - Nariman Ammar
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States.,Ochsner Xavier Institute for Health Equity and Research, Ochsner Clinic Foundation, New Orleans, LA, United States
| | - Arash Shaban-Nejad
- Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
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Noji R, Tohyama K, Kugimoto T, Kuroshima T, Hirai H, Tomioka H, Michi Y, Tasaki A, Ohno K, Ariizumi Y, Onishi I, Suenaga M, Mori T, Okamoto R, Yoshimura R, Miura M, Asakage T, Miyake S, Ikeda S, Harada H, Kano Y. Comprehensive Genomic Profiling Reveals Clinical Associations in Response to Immune Therapy in Head and Neck Cancer. Cancers (Basel) 2022; 14:cancers14143476. [PMID: 35884537 PMCID: PMC9315472 DOI: 10.3390/cancers14143476] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/15/2022] [Accepted: 07/16/2022] [Indexed: 11/17/2022] Open
Abstract
Comprehensive genomic profiling (CGP) provides information regarding cancer-related genetic aberrations. However, its clinical utility in recurrent/metastatic head and neck cancer (R/M HNC) remains unknown. Additionally, predictive biomarkers for immune checkpoint inhibitors (ICIs) should be fully elucidated because of their low response rate. Here, we analyzed the clinical utility of CGP and identified predictive biomarkers that respond to ICIs in R/M HNC. We evaluated over 1100 cases of HNC using the nationwide genetic clinical database established by the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) and 54 cases in an institution-based study. The C-CAT database revealed that 23% of the cases were candidates for clinical trials, and 5% received biomarker-matched therapy, including NTRK fusion. Our institution-based study showed that 9% of SCC cases and 25% of salivary gland cancer cases received targeted agents. In SCC cases, the tumor mutational burden (TMB) high (≥10 Mut/Mb) group showed long-term survival (>2 years) in response to ICI therapy, whereas the PD-L1 combined positive score showed no significant difference in progression-free survival. In multivariate analysis, CCND1 amplification was associated with a lower response to ICIs. Our results indicate that CGP may be useful in identifying prognostic biomarkers for immunotherapy in patients with HNC.
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Affiliation(s)
- Rika Noji
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (R.N.); (M.S.); (T.M.); (S.M.); (S.I.)
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
- Department of Precision Cancer Medicine, Center for Innovative Cancer Treatment, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Kohki Tohyama
- Department of Oral Radiation Oncology, Division of Oral Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (K.T.); (M.M.)
| | - Takuma Kugimoto
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
| | - Takeshi Kuroshima
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
| | - Hideaki Hirai
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
| | - Hirofumi Tomioka
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
| | - Yasuyuki Michi
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
| | - Akihisa Tasaki
- Department of Head and Neck Surgery, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (A.T.); (K.O.); (Y.A.); (T.A.)
| | - Kazuchika Ohno
- Department of Head and Neck Surgery, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (A.T.); (K.O.); (Y.A.); (T.A.)
| | - Yosuke Ariizumi
- Department of Head and Neck Surgery, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (A.T.); (K.O.); (Y.A.); (T.A.)
| | - Iichiroh Onishi
- Department of Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan;
| | - Mitsukuni Suenaga
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (R.N.); (M.S.); (T.M.); (S.M.); (S.I.)
| | - Takehiko Mori
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (R.N.); (M.S.); (T.M.); (S.M.); (S.I.)
- Department of Hematology, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Ryuichi Okamoto
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan;
| | - Ryoichi Yoshimura
- Department of Radiation Therapeutics and Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan;
| | - Masahiko Miura
- Department of Oral Radiation Oncology, Division of Oral Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (K.T.); (M.M.)
| | - Takahiro Asakage
- Department of Head and Neck Surgery, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (A.T.); (K.O.); (Y.A.); (T.A.)
| | - Satoshi Miyake
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (R.N.); (M.S.); (T.M.); (S.M.); (S.I.)
| | - Sadakatsu Ikeda
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (R.N.); (M.S.); (T.M.); (S.M.); (S.I.)
- Department of Precision Cancer Medicine, Center for Innovative Cancer Treatment, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
| | - Hiroyuki Harada
- Department of Oral and Maxillofacial Surgery, Division of Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (T.K.); (T.K.); (H.H.); (H.T.); (Y.M.); (H.H.)
| | - Yoshihito Kano
- Department of Clinical Oncology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan; (R.N.); (M.S.); (T.M.); (S.M.); (S.I.)
- Department of Precision Cancer Medicine, Center for Innovative Cancer Treatment, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-Ku, Tokyo 113-8510, Japan
- Correspondence: ; Tel.: +81-3-5803-5391
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Rao SJ, Born HL, Madden LL. #Laryngology: A Standardized Hashtag Ontology. J Voice 2022:S0892-1997(22)00158-8. [PMID: 35850888 DOI: 10.1016/j.jvoice.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/02/2022] [Accepted: 06/03/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND In the past decade, there has been a rise in social media applications and usage among individuals in the otolaryngology- head and neck surgery (OHNS) community. Hashtags (#), used to identify posts relating to similar topics, are utilized to search medical information, build a network, find providers, and discuss research. Previous OHNS literature in this arena includes a standard ontology, or list of hashtags, developed for the otology subspecialty. To date, the ontology of laryngology hashtags has not been created. The objective of this study is to propose a standardized ontology to use when discussing topics in laryngology on social media to maximize reach and effect. METHODS Using a combination of previously published techniques, along with laryngology specific adjustments, the authors developed a list of suggested hashtags. An initial list was systematically culled from laryngology Instagram accounts including academic programs, laryngology influencers (fellowship-trained laryngologists with publicly available professional accounts with greater than 500 followers), and professional societies/conferences. The list was abbreviated using current rate of use, specificity, and expert opinion. These were then categorized to include general terms, diseases and diagnoses, and treatment strategies RESULTS: Across all culled Instagram posts, there were 240 unique laryngology hashtags used and 1152 total hashtags were applied. The authors derived unique terms to be included in the ontology for laryngology by expert opinion of fellowship-trained laryngologists. CONCLUSION Laryngology is in the early stages of utilization of social media. Developing a specific ontology of hashtags to be used will optimize the reach and connections of term specific searches.
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Affiliation(s)
- Shambavi J Rao
- Wake Forest School of Medicine, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA
| | - Hayley L Born
- Division of Laryngology, Department of Otolaryngology - Head and Neck Surgery, Weill Cornell School of Medicine, Sean Parker Institute for the Voice, New York, New York, USA
| | - Lyndsay L Madden
- Department of Otolaryngology - Head and Neck Surgery, Wake Forest School of Medicine, Atrium Health Wake Forest Baptist Medical Center, Winston-Salem, North Carolina, USA.
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50
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Lou P, Wang C, Guo R, Yao L, Zhang G, Yang J, Yuan Y, Dong Y, Gao Z, Gong T, Li C. HistoML, a markup language for representation and exchange of histopathological features in pathology images. Sci Data 2022; 9:387. [PMID: 35803960 PMCID: PMC9270329 DOI: 10.1038/s41597-022-01505-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/23/2022] [Indexed: 11/09/2022] Open
Abstract
The study of histopathological phenotypes is vital for cancer research and medicine as it links molecular mechanisms to disease prognosis. It typically involves integration of heterogenous histopathological features in whole-slide images (WSI) to objectively characterize a histopathological phenotype. However, the large-scale implementation of phenotype characterization has been hindered by the fragmentation of histopathological features, resulting from the lack of a standardized format and a controlled vocabulary for structured and unambiguous representation of semantics in WSIs. To fill this gap, we propose the Histopathology Markup Language (HistoML), a representation language along with a controlled vocabulary (Histopathology Ontology) based on Semantic Web technologies. Multiscale features within a WSI, from single-cell features to mesoscopic features, could be represented using HistoML which is a crucial step towards the goal of making WSIs findable, accessible, interoperable and reusable (FAIR). We pilot HistoML in representing WSIs of kidney cancer as well as thyroid carcinoma and exemplify the uses of HistoML representations in semantic queries to demonstrate the potential of HistoML-powered applications for phenotype characterization.
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Affiliation(s)
- Peiliang Lou
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Chunbao Wang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, China
| | - Ruifeng Guo
- Division of Anatomic Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Lixia Yao
- Department of Health Services Administration and Policy, Temple University, Philadelphia, PA, USA
| | - Guanjun Zhang
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, Shaanxi, China
| | - Jun Yang
- Department of Pathology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 3, Shang Qin Road, Xi'an, Shaanxi, China
| | - Yong Yuan
- Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi'an Jiaotong University, 309 Yanta West Road, Xi'an, Shaanxi, China
| | - Yuxin Dong
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Zeyu Gao
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Tieliang Gong
- Key Laboratory of Intelligent Networks and Network Security (Xi'an Jiaotong University), Ministry of Education, Xi'an, Shaanxi, 710049, China
| | - Chen Li
- National Engineering Lab for Big Data Analytics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
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