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Lu L, Zhong Y, Luo S, Liu S, Xiao Z, Ding J, Shao J, Fu H, Xu J. Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications. Health Informatics J 2024; 30:14604582241262961. [PMID: 38881290 DOI: 10.1177/14604582241262961] [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: 06/18/2024]
Abstract
Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the "Service, Patient, Regression, base/Away, Yeast" (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.
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Affiliation(s)
- Lu Lu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Yun Zhong
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Shuqing Luo
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Sichen Liu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Zhongzhou Xiao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jinru Ding
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Jin Shao
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Hailong Fu
- Department of Anesthesiology, Changzheng Hospital, Naval Medical University, Shanghai, P.R. china
| | - Jie Xu
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- Université de Montpellier, Montpellier, France
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2
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Peng Y, Bathelt F, Gebler R, Gött R, Heidenreich A, Henke E, Kadioglu D, Lorenz S, Vengadeswaran A, Sedlmayr M. Use of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review. JMIR Med Inform 2024; 12:e52967. [PMID: 38354027 PMCID: PMC10902772 DOI: 10.2196/52967] [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: 09/20/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND Multisite clinical studies are increasingly using real-world data to gain real-world evidence. However, due to the heterogeneity of source data, it is difficult to analyze such data in a unified way across clinics. Therefore, the implementation of Extract-Transform-Load (ETL) or Extract-Load-Transform (ELT) processes for harmonizing local health data is necessary, in order to guarantee the data quality for research. However, the development of such processes is time-consuming and unsustainable. A promising way to ease this is the generalization of ETL/ELT processes. OBJECTIVE In this work, we investigate existing possibilities for the development of generic ETL/ELT processes. Particularly, we focus on approaches with low development complexity by using descriptive metadata and structural metadata. METHODS We conducted a literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We used 4 publication databases (ie, PubMed, IEEE Explore, Web of Science, and Biomed Center) to search for relevant publications from 2012 to 2022. The PRISMA flow was then visualized using an R-based tool (Evidence Synthesis Hackathon). All relevant contents of the publications were extracted into a spreadsheet for further analysis and visualization. RESULTS Regarding the PRISMA guidelines, we included 33 publications in this literature review. All included publications were categorized into 7 different focus groups (ie, medicine, data warehouse, big data, industry, geoinformatics, archaeology, and military). Based on the extracted data, ontology-based and rule-based approaches were the 2 most used approaches in different thematic categories. Different approaches and tools were chosen to achieve different purposes within the use cases. CONCLUSIONS Our literature review shows that using metadata-driven (MDD) approaches to develop an ETL/ELT process can serve different purposes in different thematic categories. The results show that it is promising to implement an ETL/ELT process by applying MDD approach to automate the data transformation from Fast Healthcare Interoperability Resources to Observational Medical Outcomes Partnership Common Data Model. However, the determining of an appropriate MDD approach and tool to implement such an ETL/ELT process remains a challenge. This is due to the lack of comprehensive insight into the characterizations of the MDD approaches presented in this study. Therefore, our next step is to evaluate the MDD approaches presented in this study and to determine the most appropriate MDD approaches and the way to integrate them into the ETL/ELT process. This could verify the ability of using MDD approaches to generalize the ETL process for harmonizing medical data.
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Affiliation(s)
- Yuan Peng
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | | | - Richard Gebler
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Robert Gött
- Core Unit Datenintegrationszentrum, University Medicine Greifswald, Greifswald, Germany
| | - Andreas Heidenreich
- Department for Information and Communication Technology (DICT), Data Integration Center (DIC), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
| | - Elisa Henke
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Dennis Kadioglu
- Department for Information and Communication Technology (DICT), Data Integration Center (DIC), Goethe University Frankfurt, University Hospital, Frankfurt am Main, Germany
- Institute for Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Stephan Lorenz
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Abishaa Vengadeswaran
- Institute for Medical Informatics, Goethe University Frankfurt, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
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3
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Henkel M, Horn T, Leboutte F, Trotsenko P, Dugas SG, Sutter SU, Ficht G, Engesser C, Matthias M, Stalder A, Ebbing J, Cornford P, Seifert H, Stieltjes B, Wetterauer C. Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening. PLoS One 2022; 17:e0271183. [PMID: 35857753 PMCID: PMC9299327 DOI: 10.1371/journal.pone.0271183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Accepted: 06/24/2022] [Indexed: 11/19/2022] Open
Abstract
Purpose Rising complexity of patients and the consideration of heterogeneous information from various IT systems challenge the decision-making process of urological oncologists. Siemens AI Pathway Companion is a decision support tool that provides physicians with comprehensive patient information from various systems. In the present study, we examined the impact of providing organized patient information in comprehensive dashboards on information quality, effectiveness, and satisfaction of physicians in the clinical decision-making process. Methods Ten urologists in our department performed the entire diagnostic workup to treatment decision for 10 patients in the prostate cancer screening setting. Expenditure of time, information quality, and user satisfaction during the decision-making process with AI Pathway Companion were recorded and compared to the current workflow. Results A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9% (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated information quality parameters completeness (Cohen’s d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94). Conclusion The software demonstrated that comprehensive organization of information improves physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.
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Affiliation(s)
- Maurice Henkel
- Research & Analytic Services University Hospital Basel, Basel, Switzerland
- Institute of Radiology, University Hospital Basel, Basel, Switzerland
- University Basel, Basel, Switzerland
- * E-mail:
| | - Tobias Horn
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Francois Leboutte
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Pawel Trotsenko
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Sarah Gina Dugas
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Sarah Ursula Sutter
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Georg Ficht
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Christian Engesser
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Marc Matthias
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | | | - Jan Ebbing
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Philip Cornford
- Department of Urology, Liverpool University Hospitals NHS Trust, Liverpool, United Kingdom
| | - Helge Seifert
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
| | - Bram Stieltjes
- Research & Analytic Services University Hospital Basel, Basel, Switzerland
- Institute of Radiology, University Hospital Basel, Basel, Switzerland
- University Basel, Basel, Switzerland
| | - Christian Wetterauer
- University Basel, Basel, Switzerland
- Institute of Urology, University Hospital Basel, Basel, Switzerland
- Danube Private University, Krems, Austria
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Zhang H, Lyu T, Yin P, Bost S, He X, Guo Y, Prosperi M, Hogan WR, Bian J. A scoping review of semantic integration of health data and information. Int J Med Inform 2022; 165:104834. [PMID: 35863206 DOI: 10.1016/j.ijmedinf.2022.104834] [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: 03/21/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.
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Affiliation(s)
- Hansi Zhang
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Tianchen Lyu
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Pengfei Yin
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Sarah Bost
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Xing He
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Willian R Hogan
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
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Jha AK, Mithun S, Sherkhane UB, Jaiswar V, Shi Z, Kalendralis P, Kulkarni C, M.S. D, Rajamenakshi R, Sunder G, Purandare N, Wee L, Rangarajan V, van Soest J, Dekker A. Implementation of Big Imaging Data Pipeline Adhering to FAIR Principles for Federated Machine Learning in Oncology. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3113860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Abstract
Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.
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Rubinger L, Ekhtiari S, Gazendam A, Bhandari M. Registries: Big data, bigger problems? Injury 2021:S0020-1383(21)01001-9. [PMID: 34930582 DOI: 10.1016/j.injury.2021.12.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/04/2021] [Indexed: 02/02/2023]
Abstract
Patient registries have grown in size and number along with general computing power and digitization of the healthcare world. In contrast to databases, registries are typically patient data systematically created and collected for the express purpose of answering health-related questions. Registries can be disease-, procedure-, pathology-, or product-based in nature. Registry-based studies typically fit into Level II or III in the hierarchy of evidence-based medicine. However, a recent advent in the use of registry data has been the development and execution of registry-based trials, such as the TASTE trial, which may elevate registry-based studies into the realm of Level I evidence. Some strengths of registries include the sheer volume of data, the inclusion of a diverse set of participants, and their ability to be linked to other registries and databases. Limitations of registries include variable quality of the collected data, and a lack of active follow-up (which may underestimate rates of adverse events). As with any study type, the intended design does not automatically lead to a study of a certain quality. While no specific tool exists for assessing the quality of a registry-based study, some important considerations include ensuring the registry is appropriate for the question being asked, whether the patient population is representative, the presence of an appropriate comparison group, and the validity and generalizability of the registry in question. The future of clinical registries remains to be seen, but the incorporation of big data and machine learning algorithms will certainly play an important role.
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Affiliation(s)
- Luc Rubinger
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada.
| | - Seper Ekhtiari
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
| | - Aaron Gazendam
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
| | - Mohit Bhandari
- Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
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Abstract
Many organizations must produce many reports for various reasons. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Furthermore, different professional figures are involved in the process, possibly with different skills (database technicians, domain experts, employees): the lack of common knowledge and of a unifying framework significantly obstructs the effective and efficient definition and continuous generation of reports. This paper presents a novel framework named RADAR, which is the acronym for “Resilient Application for Dependable Aided Reporting”: the framework has been devised to be a ”bridge” between data and employees in charge of generating reports. Specifically, it builds a common knowledge base in which database administrators and domain experts describe their knowledge about the application domain and the gathered data; this knowledge can be browsed by employees to find out the relevant data to aggregate and insert into reports, while designing report layouts; the framework assists the overall process from data definition to report generation. The paper presents the application scenario and the vision by means of a running example, defines the data model and presents the architecture of the framework.
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9
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Reimer AP, Dai W, Smith B, Schiltz NK, Sun J, Koroukian SM. Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models. Int J Med Inform 2021; 156:104588. [PMID: 34607290 DOI: 10.1016/j.ijmedinf.2021.104588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/17/2021] [Accepted: 09/19/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses associated with a patient's clinical encounter. The purpose of this study was to assess the feasibility of developing a schema to identify and subclassify all structured diagnosis codes for a patient encounter. METHODS To develop a subclassification schema we used EHR data from an interhospital transport data repository that contained complete hospital encounter level data. Eight discrete data sources containing structured diagnosis codes were identified. Diagnosis codes were normalized using the Unified Medical Language System and additional EHR data were combined with standardized terminologies to create and validate the subcategories. We then employed random forest to assess the usefulness of the new subcategorized diagnoses to predict post-interhospital transfer mortality by building 2 models, one using standard diagnosis codes, and one using the new subcategorized diagnosis codes. RESULTS Six subcategories of diagnoses were identified and validated. The subcategories included: primary or admitting diagnoses (10%), past medical, surgical or social history (9%), problem list (20%), comorbidity (24%), discharge diagnoses (6%), and unmapped diagnoses (31%). The subcategorized model outperformed the standard model, achieving a training AUROC of 0.97 versus 0.95 and testing model AUROC of 0.81 versus 0.46. DISCUSSION Our work demonstrates that merging structured diagnosis codes with additional EHR data and secondary data sources provides additional information to understand the role of diagnosis throughout a clinical encounter and improves predictive model performance. Further work is necessary to assess if subcategorizing produces benefits in interpreting the results of prognostic models and/or operationalizing the results in clinical decision support applications.
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Affiliation(s)
- Andrew P Reimer
- Frances Payne Bolton School of Nursing, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, United States; Critical Care Transport, Cleveland Clinic, 9800 Euclid Ave, Cleveland, OH, United States.
| | - Wei Dai
- Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Benjamin Smith
- Department of Mathematics, Applied Mathematics and Statistics, College of Arts and Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Nicholas K Schiltz
- Frances Payne Bolton School of Nursing, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, United States
| | - Jiayang Sun
- Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Siran M Koroukian
- Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
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Gaudet-Blavignac C, Raisaro JL, Touré V, Österle S, Crameri K, Lovis C. A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study. JMIR Med Inform 2021; 9:e27591. [PMID: 34185008 PMCID: PMC8277320 DOI: 10.2196/27591] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/27/2021] [Accepted: 05/19/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Interoperability is a well-known challenge in medical informatics. Current trends in interoperability have moved from a data model technocentric approach to sustainable semantics, formal descriptive languages, and processes. Despite many initiatives and investments for decades, the interoperability challenge remains crucial. The need for data sharing for most purposes ranging from patient care to secondary uses, such as public health, research, and quality assessment, faces unmet problems. OBJECTIVE This work was performed in the context of a large Swiss Federal initiative aiming at building a national infrastructure for reusing consented data acquired in the health care and research system to enable research in the field of personalized medicine in Switzerland. The initiative is the Swiss Personalized Health Network (SPHN). This initiative is providing funding to foster use and exchange of health-related data for research. As part of the initiative, a national strategy to enable a semantically interoperable clinical data landscape was developed and implemented. METHODS A deep analysis of various approaches to address interoperability was performed at the start, including large frameworks in health care, such as Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE), and in several domains, such as regulatory agencies (eg, Clinical Data Interchange Standards Consortium [CDISC]) and research communities (eg, Observational Medical Outcome Partnership [OMOP]), to identify bottlenecks and assess sustainability. Based on this research, a strategy composed of three pillars was designed. It has strong multidimensional semantics, descriptive formal language for exchanges, and as many data models as needed to comply with the needs of various communities. RESULTS This strategy has been implemented stepwise in Switzerland since the middle of 2019 and has been adopted by all university hospitals and high research organizations. The initiative is coordinated by a central organization, the SPHN Data Coordination Center of the SIB Swiss Institute of Bioinformatics. The semantics is mapped by domain experts on various existing standards, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and International Classification of Diseases (ICD). The resource description framework (RDF) is used for storing and transporting data, and to integrate information from different sources and standards. Data transformers based on SPARQL query language are implemented to convert RDF representations to the numerous data models required by the research community or bridge with other systems, such as electronic case report forms. CONCLUSIONS The SPHN strategy successfully implemented existing standards in a pragmatic and applicable way. It did not try to build any new standards but used existing ones in a nondogmatic way. It has now been funded for another 4 years, bringing the Swiss landscape into a new dimension to support research in the field of personalized medicine and large interoperable clinical data.
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Affiliation(s)
- Christophe Gaudet-Blavignac
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Jean Louis Raisaro
- Data Science Group, Division of Information Systems, Lausanne University Hospital, Lausanne, Switzerland
- Precision Medicine Unit, Department of Laboratories, Lausanne University Hospital, Lausanne, Switzerland
| | - Vasundra Touré
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Sabine Österle
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Katrin Crameri
- Personalized Health Informatics Group, SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Christian Lovis
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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11
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Li X, Han J, Zhang S, Chen K, Zhao L, He Y, Liu S. Artificial Intelligence for Screening Chinese Electronic Medical Record and Biobank Information. Biopreserv Biobank 2021; 19:386-393. [PMID: 34042506 DOI: 10.1089/bio.2020.0151] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Objective: To establish a structured and integrated platform of clinical data and biobank data, and a client to retrieve these data. Study Design: Initially, the hospital information system (HIS) and biobank information system (BIS) were integrated through the patients' ID numbers. Then, natural language processing (NLP) was used to process the integrated unstructured clinical information. A query interface was designed for this system, which enabled researchers to retrieve clinical or biobank data. Finally, several queries were listed and manually checked to test the retrieval performance of the system. Results: The construction of the biobank screening system (BSS) was completed, and the data were structured. The BSS took an average of 2 seconds to perform a search for target patients/samples. The retrieval results were consistent with the HIS and BIS. For complex queries, we manually checked the retrieved patients/samples, and the system's accuracy was 100%. Conclusion: This NLP-based system improved biological sample screening and using of clinical data. We will continue to improve this system, enhance resource sharing, and promote the development of translational medicine.
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Affiliation(s)
- Xiaoqing Li
- Department of Biobank, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiang Han
- Department of Biobank, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shaodian Zhang
- Synyi Research, Shanghai, China.,APEX Data and Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China
| | | | - Liebin Zhao
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Child Health Advocacy Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi He
- Department of Informatics, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shijian Liu
- Department of Biobank, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Child Health Advocacy Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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12
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Pedrera-Jiménez M, García-Barrio N, Cruz-Rojo J, Terriza-Torres AI, López-Jiménez EA, Calvo-Boyero F, Jiménez-Cerezo MJ, Blanco-Martínez AJ, Roig-Domínguez G, Cruz-Bermúdez JL, Bernal-Sobrino JL, Serrano-Balazote P, Muñoz-Carrero A. Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models. J Biomed Inform 2021; 115:103697. [PMID: 33548541 PMCID: PMC7857038 DOI: 10.1016/j.jbi.2021.103697] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/18/2020] [Accepted: 02/01/2021] [Indexed: 10/27/2022]
Abstract
BACKGROUND COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization. OBJECTIVE This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time. MATERIAL AND METHODS The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020. RESULTS An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts. CONCLUSION This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs.
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Affiliation(s)
- Miguel Pedrera-Jiménez
- Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, 28041 Madrid, Spain; ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
| | | | - Jaime Cruz-Rojo
- Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, 28041 Madrid, Spain.
| | | | | | | | | | | | | | | | | | | | - Adolfo Muñoz-Carrero
- Digital Health Research Dept., Instituto de Salud Carlos III, Av. de Monforte de Lemos, 5, 28029 Madrid, Spain.
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Zimmermann R, Becker A, Hackstein H, Ganslandt T. Erstellung zuverlässiger Erythrozytenkonzentrat-Bedarfslisten für Operationen und Interventionen aus Data-Warehouse-Daten und der patientenbezogenen Chargendokumentation von Blutkomponenten. TRANSFUSIONSMEDIZIN 2021. [DOI: 10.1055/a-1256-4465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
ZusammenfassungDas Versäumnis, transfusionsvorbereitende Diagnostik durchzuführen und eine ausreichende Anzahl von Blutkomponenten vor der Operation zu bestellen, führt zunehmend zu klinischen Notfällen und vermeidbaren Risiken für die Patienten. Die eigentlich obligatorische Erstellung von Blutbedarfslisten scheitert oft an der Verfügbarkeit geeigneter Daten. Das Universitätsklinikum Erlangen ist ein Krankenhaus der Tertiärversorgung mit 1400 Betten. Hier werden seit 2010 kontinuierlich alle relevanten Daten zur Interpretation von Mustern bei der Verwendung von Blutbestandteilen erhoben. Wir sind in der Lage, die klinischen Data-Warehouse-Komponenten der persönlichen Daten der Empfänger von Erythrozytenkonzentraten (EK), die Codes der stationären Patienten im G-DRG-System (G-DRG: German Diagnosis Related Group), ICD-Codes, OPS-Codes und die Daten der EK-Komponenten aus dem IT-System der Blutbank zu integrieren. Die erhaltenen DRGs, ICD-Codes und OPS-Codes werden mit den Daten des
Blutkomponentenverbrauchs auf individueller Basis verknüpft. Analysen können sowohl im gesamten Krankenhaus als auch in Bezug auf bestimmte Abteilungen durchgeführt werden. Die laufende Verfeinerung der Datenbankabfragen verbessert die Fähigkeit, den Blutbedarf für Standardoperationen und Interventionen korrekt abzuschätzen. Die Ergebnisse weichen zum Teil erheblich von den Schätzungen der Operateure hinsichtlich der Wahrscheinlichkeit einer Transfusion ab, oftmals wird die Wahrscheinlichkeit einer Transfusion unterschätzt. Die korrekte Einhaltung der 10-prozentigen Transfusionswahrscheinlichkeitsgrenze für die Frage der frühen präoperativen Bereitstellung von Kreuzblut kann durch solche Abfragen verbessert werden. Dem Phänomen der „verzögerten Transfusion“ entgegenzuwirken, wird zu einer immer wichtigeren Aufgabe für den klinisch tätigen transfusionsmedizinischen Arzt. Die Erstellung von Statistiken über die Transfusionswahrscheinlichkeit durch die Verknüpfung von
Data-Warehouse-Daten und Chargendokumentationsdaten aus Blutbanken kann die notwendigen Werkzeuge bereitstellen.
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Affiliation(s)
- Robert Zimmermann
- Universitätsklinikum Erlangen, Transfusionsmedizinische und Hämostaseologische Abteilung, Erlangen, Deutschland
| | - Andreas Becker
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Lehrstuhl für Medizinische Informatik, Erlangen, Deutschland
| | - Holger Hackstein
- Universitätsklinikum Erlangen, Transfusionsmedizinische und Hämostaseologische Abteilung, Erlangen, Deutschland
| | - Thomas Ganslandt
- Ruprecht-Karls-Universität Heidelberg, Heinrich Lanz-Zentrum für Digitale Gesundheit, Medizinische Fakultät, Mannheim, Deutschland
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Domingues MAP, Camacho R, Rodrigues PP. CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens. J Biomed Inform 2020; 114:103669. [PMID: 33359111 DOI: 10.1016/j.jbi.2020.103669] [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: 09/16/2020] [Revised: 11/28/2020] [Accepted: 12/16/2020] [Indexed: 11/27/2022]
Abstract
Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism.
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Affiliation(s)
| | - Rui Camacho
- Faculty of Engineering of the University of Porto, Portugal; LIAAD-INESC TEC, Porto, Portugal
| | - Pedro Pereira Rodrigues
- CINTESIS - Center for Health Technology and Services Research, Portugal; Faculty of Medicine of the University of Porto, Portugal
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Zheng L, Min H, Chen Y, Keloth V, Geller J, Perl Y, Hripcsak G. Outlier concepts auditing methodology for a large family of biomedical ontologies. BMC Med Inform Decis Mak 2020; 20:296. [PMID: 33319713 PMCID: PMC7737254 DOI: 10.1186/s12911-020-01311-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Summarization networks are compact summaries of ontologies. The "Big Picture" view offered by summarization networks enables to identify sets of concepts that are more likely to have errors than control concepts. For ontologies that have outgoing lateral relationships, we have developed the "partial-area taxonomy" summarization network. Prior research has identified one kind of outlier concepts, concepts of small partials-areas within partial-area taxonomies. Previously we have shown that the small partial-area technique works successfully for four ontologies (or their hierarchies). METHODS To improve the Quality Assurance (QA) scalability, a family-based QA framework, where one QA technique is potentially applicable to a whole family of ontologies with similar structural features, was developed. The 373 ontologies hosted at the NCBO BioPortal in 2015 were classified into a collection of families based on structural features. A meta-ontology represents this family collection, including one family of ontologies having outgoing lateral relationships. The process of updating the current meta-ontology is described. To conclude that one QA technique is applicable for at least half of the members for a family F, this technique should be demonstrated as successful for six out of six ontologies in F. We describe a hypothesis setting the condition required for a technique to be successful for a given ontology. The process of a study to demonstrate such success is described. This paper intends to prove the scalability of the small partial-area technique. RESULTS We first updated the meta-ontology classifying 566 BioPortal ontologies. There were 371 ontologies in the family with outgoing lateral relationships. We demonstrated the success of the small partial-area technique for two ontology hierarchies which belong to this family, SNOMED CT's Specimen hierarchy and NCIt's Gene hierarchy. Together with the four previous ontologies from the same family, we fulfilled the "six out of six" condition required to show the scalability for the whole family. CONCLUSIONS We have shown that the small partial-area technique can be potentially successful for the family of ontologies with outgoing lateral relationships in BioPortal, thus improve the scalability of this QA technique.
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Affiliation(s)
- Ling Zheng
- Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, 07764, USA.
| | - Hua Min
- Department of Health Administration and Policy, George Mason University, Fairfax, VA, 22030, USA
| | - Yan Chen
- CIS Department, Borough of Manhattan Community College, CUNY, New York, NY, 10007, USA
| | - Vipina Keloth
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - James Geller
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - Yehoshua Perl
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, 07102, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA
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16
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Hammad R, Barhoush M, Abed-alguni BH. A Semantic-Based Approach for Managing Healthcare Big Data: A Survey. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8865808. [PMID: 33489061 PMCID: PMC7787845 DOI: 10.1155/2020/8865808] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/02/2020] [Accepted: 11/09/2020] [Indexed: 12/20/2022]
Abstract
Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.
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17
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Abstract
Improved health care services can benefit from a more seamless exchange of medical information between patients and health care providers. This exchange is especially important considering the increasing trends in mobility, comorbidity and outbreaks. However, current Electronic Health Records (EHR) tend to be institution-centric, often leaving the medical information of the patient fragmented and more importantly inaccessible to the patient for sharing with other health providers in a timely manner. Nearly a decade ago, several client–server models for personal health records (PHR) were proposed. The aim of these previous PHRs was to address data fragmentation issues. However, these models were not widely adopted by patients. This paper discusses the need for a new PHR model that can enhance the patient experience by making medical services more accessible. The aims of the proposed model are to (1) help patients maintain a complete lifelong health record, (2) facilitate timely communication and data sharing with health care providers from multiple institutions and (3) promote integration with advanced third-party services (e.g., risk prediction for chronic diseases) that require access to the patient’s health data. The proposed model is based on a Peer-to-Peer (P2P) network as opposed to the client–server architecture of the previous PHR models. This architecture consists of a central index server that manages the network and acts as a mediator, a peer client for patients and providers that allows them to manage health records and connect to the network, and a service client that enables third-party providers to offer services to the patients. This distributed architecture is essential since it promotes ownership of the health record by the patient instead of the health care institution. Moreover, it allows the patient to subscribe to an extended range of personalized e-health services.
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18
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BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials. Med Biol Eng Comput 2020; 58:2339-2354. [DOI: 10.1007/s11517-020-02201-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 05/22/2020] [Indexed: 10/23/2022]
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19
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Sima AC, Mendes de Farias T, Zbinden E, Anisimova M, Gil M, Stockinger H, Stockinger K, Robinson-Rechavi M, Dessimoz C. Enabling semantic queries across federated bioinformatics databases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5614223. [PMID: 31697362 PMCID: PMC6836710 DOI: 10.1093/database/baz106] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/01/2019] [Accepted: 08/02/2019] [Indexed: 11/23/2022]
Abstract
Motivation: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. Results: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.
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Affiliation(s)
- Ana Claudia Sima
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Tarcisio Mendes de Farias
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Erich Zbinden
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Maria Anisimova
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Manuel Gil
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Heinz Stockinger
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Kurt Stockinger
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland
| | - Marc Robinson-Rechavi
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Genetics, Evolution, and Environment, University College London, Gower St, London WC1E 6BT, UK.,Department of Computer Science, University College London, Gower St, London WC1E 6BT, UK
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20
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Freedman HG, Williams H, Miller MA, Birtwell D, Mowery DL, Stoeckert CJ. A novel tool for standardizing clinical data in a semantically rich model. J Biomed Inform 2020; 112S:100086. [PMID: 34417005 DOI: 10.1016/j.yjbinx.2020.100086] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 11/18/2022]
Abstract
Standardizing clinical information in a semantically rich data model is useful for promoting interoperability and facilitating high quality research. Semantic Web technologies such as Resource Description Framework can be utilized to their full potential when a model accurately reflects the semantics of the clinical situation it describes. To this end, ontologies that abide by sound organizational principles can be used as the building blocks of a semantically rich model for the storage of clinical data. However, it is a challenge to programmatically define such a model and load data from disparate sources. The PennTURBO Semantic Engine is a tool developed at the University of Pennsylvania that transforms concise RDF data into a source-independent, semantically rich model. This system sources classes from an application ontology and specifically defines how instances of those classes may relate to each other. Additionally, the system defines and executes RDF data transformations by launching dynamically generated SPARQL update statements. The Semantic Engine was designed as a generalizable data standardization tool, and is able to work with various data models and incoming data sources. Its human-readable configuration files can easily be shared between institutions, providing the basis for collaboration on a standard data model.
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Affiliation(s)
- Hayden G Freedman
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, United States
| | - Heather Williams
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, United States
| | - Mark A Miller
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, United States
| | - David Birtwell
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, United States
| | - Danielle L Mowery
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, United States; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, United States
| | - Christian J Stoeckert
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104, United States; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, 415 Curie Boulevard, Philadelphia, PA 19104, United States
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21
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An Ontological Approach to Knowledge Building by Data Integration. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7304717 DOI: 10.1007/978-3-030-50436-6_35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
This paper discusses the uncertainty in the automation of knowledge building from heterogeneous raw datasets. Ontologies play a critical role in such a process by providing a well consolidated support to link and semantically integrate datasets via interoperability, as well as semantic enrichment and annotations. By adopting Semantic Web technology, the resulting ecosystem is fully machine consumable. However, while the manual alignment of concepts from different vocabularies is reasonable at a small scale, fully automatic mechanisms are required once the target system scales up, leading to a significant uncertainty.
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von Lucadou M, Ganslandt T, Prokosch HU, Toddenroth D. Feasibility analysis of conducting observational studies with the electronic health record. BMC Med Inform Decis Mak 2019; 19:202. [PMID: 31660955 PMCID: PMC6819452 DOI: 10.1186/s12911-019-0939-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 10/16/2019] [Indexed: 12/20/2022] Open
Abstract
Background The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes. Methods After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions “completeness”, “correctness”, “currency” and “granularity”. Results Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n = 411, Study 2 n = 423, Study 3 n = 13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions. Conclusions While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.
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Affiliation(s)
- Marcel von Lucadou
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
| | - Thomas Ganslandt
- Department of Biomedical Informatics, Mannheim University Medicine, Ruprecht-Karls-University Heidelberg, Mannheim, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dennis Toddenroth
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Mate S, Kampf M, Rödle W, Kraus S, Proynova R, Silander K, Ebert L, Lablans M, Schüttler C, Knell C, Eklund N, Hummel M, Holub P, Prokosch HU. Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC. Appl Clin Inform 2019; 10:679-692. [PMID: 31509880 PMCID: PMC6739205 DOI: 10.1055/s-0039-1695793] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Background
High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks.
Objectives
To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task.
Methods
Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application.
Results
The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients.
Conclusion
A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.
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Affiliation(s)
- Sebastian Mate
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Marvin Kampf
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Wolfgang Rödle
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan Kraus
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Rumyana Proynova
- Medical Informatics in Translational Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Kaisa Silander
- Genomics and Biobank Unit, Finnish National Institute for Health and Welfare, Helsinki, Finland
| | - Lars Ebert
- Federated Information Systems, German Cancer Research Center, Heidelberg, Germany
| | - Martin Lablans
- Federated Information Systems, German Cancer Research Center, Heidelberg, Germany
| | - Christina Schüttler
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Christian Knell
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Niina Eklund
- Genomics and Biobank Unit, Finnish National Institute for Health and Welfare, Helsinki, Finland
| | - Michael Hummel
- Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Biobanking and BioMolecular Resources Research Infrastructure (BBMRI-ERIC), Graz, Austria
| | - Petr Holub
- Biobanking and BioMolecular Resources Research Infrastructure (BBMRI-ERIC), Graz, Austria
| | - Hans-Ulrich Prokosch
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.,Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Abstract
Ontology fusion in knowledge bases has become less easy, due to the massive capacity involved in the process of semantic similarity calculation. Many similarity calculation methods have been developed, although they are hardly united. This article contributes a low-cost similarity calculation method for ontology fusion, based on the inspiration of binary metrics, with the aim of reducing the size of similarity calculations both spatially and logically. By introducing the definitions of a heterogeneous ontology, entities of ontologies and rules of ontology fusion on the basis of concept fusion and relationship fusion, we put forward the algorithm of main traverse procedure and calculated to be the least cost in time and space in comparison with traditional methods. We adopted three experiments to testify the usability of our approach from the perspective of actual library resources, small datasets and large datasets. In Experiment 1, the bibliographic data from East China Normal University Library were used to show the feasibility and capability of our proposal and present the process of the algorithm. In both Experiments 2 and 3, our approach had at least 88% confidence in detecting accurate merging mappings and also decreased time cost. The test demonstrated a good fusion result. The problem of lower recalls caused by error analysis results from the conflict between the complex structures in ontologies and the recursive functions, which will be improved in the future.
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Affiliation(s)
- Wen Lou
- Department of Information Management, Faculty of Economics and Management, East China Normal University, China
- Institute for Academic Evaluation and Development, East China Normal University, China
| | - Ruofan Pi
- Department of Accounting, Faculty of Economics and Management, East China Normal University, China
| | - Hui Wang
- Library, Zhejiang College of Construction, China
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25
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Bona JP, Prior FW, Zozus MN, Brochhausen M. Enhancing Clinical Data and Clinical Research Data with Biomedical Ontologies - Insights from the Knowledge Representation Perspective. Yearb Med Inform 2019; 28:140-151. [PMID: 31419826 PMCID: PMC6697506 DOI: 10.1055/s-0039-1677912] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVES There exists a communication gap between the biomedical informatics community on one side and the computer science/artificial intelligence community on the other side regarding the meaning of the terms "semantic integration" and "knowledge representation". This gap leads to approaches that attempt to provide one-to-one mappings between data elements and biomedical ontologies. Our aim is to clarify the representational differences between traditional data management and semantic-web-based data management by providing use cases of clinical data and clinical research data re-representation. We discuss how and why one-to-one mappings limit the advantages of using Semantic Web Technologies (SWTs). METHODS We employ commonly used SWTs, such as Resource Description Framework (RDF) and Ontology Web Language (OWL). We reuse pre-existing ontologies and ensure shared ontological commitment by selecting ontologies from a framework that fosters community-driven collaborative ontology development for biomedicine following the same set of principles. RESULTS We demonstrate the results of providing SWT-compliant re-representation of data elements from two independent projects managing clinical data and clinical research data. Our results show how one-to-one mappings would hinder the exploitation of the advantages provided by using SWT. CONCLUSIONS We conclude that SWT-compliant re-representation is an indispensable step, if using the full potential of SWT is the goal. Rather than providing one-to-one mappings, developers should provide documentation that links data elements to graph structures to specify the re-representation.
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Affiliation(s)
| | - Fred W. Prior
- University of Arkansas for Medical Sciences, Arkansas, USA
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26
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Vega-Gorgojo G, Slaughter L, Giese M. Seeing the whole picture: integrated pre-surgery reports with PreOptique. J Biomed Semantics 2019; 10:5. [PMID: 30832727 PMCID: PMC6398264 DOI: 10.1186/s13326-019-0197-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 02/18/2019] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Information technology has transformed the way healthcare is conducted. There is a deluge of patient data dispersed in different systems that are commonly not interoperable. As a result, access to patient data has become a major bottleneck for healthcare professionals that struggle to find the relevant information in a timely way and without missing critical clinical information. RESULTS We implemented PreOptique, a novel hybrid semantic and text-based system that was commissioned by a large hospital in Norway for providing integrated access to patient health records scattered over several databases and document repositories. We use ontology-based data access (OBDA) for the seamless integration of the structured databases at the hospital through the Optique platform. We employ text analysis techniques to extract vital sign measures and clinical findings from patient documents. PreOptique was developed and deployed at the hospital. This solution demonstrates how OBDA technology can provide integrated data access to disparate structured sources in healthcare, without requiring the replacement of existing databases. Unstructured clinical texts are also mined to extract patient findings, while the graphical user interface (GUI) provides a single access point that hides the underlying complexity of the system. We ran a usability study with 5 target users, obtaining a system usability score (SUS) of 86.0. Further, participants in the study stressed the simplicity of the GUI and the integration of data sources enabled by the system. CONCLUSIONS This pilot study showcases the use of OBDA technology and text analysis to enable the integration of patient data for supporting clinical surgery operations. PreOptique is usable and can be easily employed by medical personnel to find patient data in a timely way.
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Affiliation(s)
- Guillermo Vega-Gorgojo
- Department of Informatics, University of Oslo, Oslo, Norway
- Department of Signal Theory and Communications and Telematics, University of Valladolid, Valladolid, Spain
| | | | - Martin Giese
- Department of Informatics, University of Oslo, Oslo, Norway
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27
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Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:5640685. [PMID: 30800257 PMCID: PMC6360056 DOI: 10.1155/2019/5640685] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 09/18/2018] [Accepted: 12/05/2018] [Indexed: 01/15/2023]
Abstract
The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.
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28
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Pop B, Fetica B, Blaga ML, Trifa AP, Achimas-Cadariu P, Vlad CI, Achimas-Cadariu A. The role of medical registries, potential applications and limitations. Med Pharm Rep 2019; 92:7-14. [PMID: 30957080 PMCID: PMC6448488 DOI: 10.15386/cjmed-1015] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 07/19/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Medical registries provide highly reliable data, challenged hierarchically only by randomized controlled trials. Although registries have been used in several fields of medicine for more than a century and a half, their key role is frequently overlooked and poorly recognized. Medical registries have evolved from calculating basic epidemiological data (incidence, prevalence, mortality) to diverse applications in disease prevention, early diagnosis and screening programs, treatment response, health care planning, decision making and disease control programs. Implementing, maintaining and running a medical registry requires substantial effort. Developing the registry represents a complex task and is one of the major barriers in widespread use of registries. Medical registries have potential to evolve to a next generation by taking benefit from recent semantic web technology developments. This paper is aimed at providing a summary of the basic information available on medical registries and to highlight the progress and potential applications in this field.
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Affiliation(s)
- Bogdan Pop
- Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Pathology, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Bogdan Fetica
- Department of Pathology, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Mihaiela Luminita Blaga
- Department of Information Technology, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca Cluj-Napoca, Romania
| | - Adrian Pavel Trifa
- Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Genetic Explorations, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca Cluj-Napoca, Romania
| | - Patriciu Achimas-Cadariu
- Department of Surgical and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Surgery, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Catalin Ioan Vlad
- Department of Surgical and Gynecological Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Surgery, "Prof. Dr. Ion Chiricuta" Oncology Institute, Cluj-Napoca, Romania
| | - Andrei Achimas-Cadariu
- Department of Medical Informatics and Biostatistics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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29
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Seneviratne MG, Kahn MG, Hernandez-Boussard T. Merging heterogeneous clinical data to enable knowledge discovery. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2019; 24:439-443. [PMID: 30864344 PMCID: PMC6447393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The vision of precision medicine relies on the integration of large-scale clinical, molecular and environmental datasets. Data integration may be thought of along two axes: data fusion across institutions, and data fusion across modalities. Cross-institutional data sharing that maintains semantic integrity hinges on the adoption of data standards and a push toward ontology-driven integration. The goal should be the creation of query-able data repositories spanning primary and tertiary care providers, disease registries, research organizations etc. to produce rich longitudinal datasets. Cross-modality sharing involves the integration of multiple data streams, from structured EHR data (diagnosis codes, laboratory tests) to genomics, imaging, monitors and patient-generated data including wearable devices. This integration presents unique technical, semantic, and ethical challenges; however recent work suggests that multi-modal clinical data can significantly improve the performance of phenotyping and prediction algorithms, powering knowledge discovery at the patient- and population-level.
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Affiliation(s)
- Martin G. Seneviratne
- Department of Biomedical Data Science, Stanford University, 1265 Welch Rd, Stanford, CA 94305, United States,
| | - Michael G. Kahn
- Colorado Clinical and Translational Sciences Institute, Denver, CO 80045, United States,
| | - Tina Hernandez-Boussard
- Department of Medicine, Biomedical Informatics, Stanford University, 1265 Welch Rd, Stanford, CA 94305, United States,
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30
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Quon H, McNutt T, Lee J, Bowers M, Jiang W, Lakshminarayanan P, Cheng Z, Han P, Hui X, Shah V, Moore J, Nakatsugawa M, Robertson S, Cecil E, Page B, Kiess A, Wong J, DeWeese T. Needs and Challenges for Radiation Oncology in the Era of Precision Medicine. Int J Radiat Oncol Biol Phys 2018; 103:809-817. [PMID: 30562547 DOI: 10.1016/j.ijrobp.2018.11.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 09/17/2018] [Accepted: 11/10/2018] [Indexed: 01/19/2023]
Abstract
Modern medicine, including the care of the cancer patient, has significantly advanced, with the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now also recognize the tremendous heterogeneity not only of disease states but of the patient and his or her environment as it influences treatment outcomes and toxicities. These reasons and many others have led to a reevaluation of the generalizability of randomized trials and growing interest in accounting for this heterogeneity under the rubric of precision medicine as it relates to personalizing clinical care predictions, decisions, and therapy for the disease state. For the cancer patient treated with radiation therapy, characterizing the spatial treatment heterogeneity has been a fundamental tenet of routine clinical care facilitated by established database and imaging platforms. Leveraging these platforms to further characterize and collate all clinically relevant sources of heterogeneity that affect the longitudinal health outcomes of the irradiated cancer patient provides an opportunity to generate a critical informatics infrastructure on which precision radiation therapy may be realized. In doing so, data science-driven insight discoveries, personalized clinical decisions, and the potential to accelerate translational efforts may be realized ideally within a network of institutions with locally developed yet coordinated informatics infrastructures. The path toward realizing these goals has many needs and challenges, which we summarize, with many still to be realized and understood. Early efforts by our group have identified the feasibility of this approach using routine clinical data sets and offer promise that this transformation can be successfully realized in radiation oncology.
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Affiliation(s)
- Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
| | - Todd McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Michael Bowers
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Wei Jiang
- Department of Civil Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Zhi Cheng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Xuan Hui
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Veeraj Shah
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Joseph Moore
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Minoru Nakatsugawa
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Scott Robertson
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Emilie Cecil
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Brandi Page
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Ana Kiess
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - John Wong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Theodore DeWeese
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
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31
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Shen F, Liu S, Wang Y, Wen A, Wang L, Liu H. Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches. JMIR Med Inform 2018; 6:e11301. [PMID: 30305261 PMCID: PMC6231873 DOI: 10.2196/11301] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 08/19/2018] [Accepted: 09/08/2018] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND In the United States, a rare disease is characterized as the one affecting no more than 200,000 patients at a certain period. Patients suffering from rare diseases are often either misdiagnosed or left undiagnosed, possibly due to insufficient knowledge or experience with the rare disease on the part of clinical practitioners. With an exponentially growing volume of electronically accessible medical data, a large volume of information on thousands of rare diseases and their potentially associated diagnostic information is buried in electronic medical records (EMRs) and medical literature. OBJECTIVE This study aimed to leverage information contained in heterogeneous datasets to assist rare disease diagnosis. Phenotypic information of patients existed in EMRs and biomedical literature could be fully leveraged to speed up diagnosis of diseases. METHODS In our previous work, we advanced the use of a collaborative filtering recommendation system to support rare disease diagnostic decision making based on phenotypes derived solely from EMR data. However, the influence of using heterogeneous data with collaborative filtering was not discussed, which is an essential problem while facing large volumes of data from various resources. In this study, to further investigate the performance of collaborative filtering on heterogeneous datasets, we studied EMR data generated at Mayo Clinic as well as published article abstracts retrieved from the Semantic MEDLINE Database. Specifically, in this study, we designed different data fusion strategies from heterogeneous resources and integrated them with the collaborative filtering model. RESULTS We evaluated performance of the proposed system using characterizations derived from various combinations of EMR data and literature, as well as with sole EMR data. We extracted nearly 13 million EMRs from the patient cohort generated between 2010 and 2015 at Mayo Clinic and retrieved all article abstracts from the semistructured Semantic MEDLINE Database that were published till the end of 2016. We applied a collaborative filtering model and compared the performance generated by different metrics. Log likelihood ratio similarity combined with k-nearest neighbor on heterogeneous datasets showed the optimal performance in patient recommendation with area under the precision-recall curve (PRAUC) 0.475 (string match), 0.511 (systematized nomenclature of medicine [SNOMED] match), and 0.752 (Genetic and Rare Diseases Information Center [GARD] match). Log likelihood ratio similarity also performed the best with mean average precision 0.465 (string match), 0.5 (SNOMED match), and 0.749 (GARD match). Performance of rare disease prediction was also demonstrated by using the optimal algorithm. Macro-average F-measure for string, SNOMED, and GARD match were 0.32, 0.42, and 0.63, respectively. CONCLUSIONS This study demonstrated potential utilization of heterogeneous datasets in a collaborative filtering model to support rare disease diagnosis. In addition to phenotypic-based analysis, in the future, we plan to further resolve the heterogeneity issue and reduce miscommunication between EMR and literature by mining genotypic information to establish a comprehensive disease-phenotype-gene network for rare disease diagnosis.
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Affiliation(s)
- Feichen Shen
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Sijia Liu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Yanshan Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Andrew Wen
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Liwei Wang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
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32
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Mathé E, Hays JL, Stover DG, Chen JL. The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources. Yearb Med Inform 2018; 27:211-222. [PMID: 30157526 PMCID: PMC6115204 DOI: 10.1055/s-0038-1667085] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology. METHODS In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome. RESULTS Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player. CONCLUSIONS The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.
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Affiliation(s)
- Ewy Mathé
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - John L. Hays
- Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
- Department of Obstetrics and Gynecology, The Ohio State University, Columbus, OH, USA
| | - Daniel G. Stover
- Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
| | - James L. Chen
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
- Department of Internal Medicine, The Ohio State University, Columbus, OH, USA
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33
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Zhang H, Guo Y, Li Q, George TJ, Shenkman E, Modave F, Bian J. An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival. BMC Med Inform Decis Mak 2018; 18:41. [PMID: 30066664 PMCID: PMC6069766 DOI: 10.1186/s12911-018-0636-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. METHODS Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative Data Analysis (IDA) needs. RESULTS Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. CONCLUSIONS Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to all users of the same datasets. Such an approach resolves the ambiguity in variable selection, extraction and integration processes and thus improve reproducibility of the IDA.
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Affiliation(s)
- Hansi Zhang
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Qian Li
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Thomas J George
- Division of Hematology and Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Elizabeth Shenkman
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - François Modave
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Clinical and Translational Research Building Suite 3228, 2004 Mowry Road, PO Box 100219, Gainesville, FL, 32610-0219, USA.
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Abburu S. Ontology Driven Cross-Linked Domain Data Integration and Spatial Semantic Multi Criteria Query System for Geospatial Public Health. INT J SEMANT WEB INF 2018. [DOI: 10.4018/ijswis.2018070101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article describes how public health information management is an interdisciplinary application which deals with cross linked application domains. Geospatial environment, place and meteorology parameters effect public health. Effective decision making plays a vital role and requires disease data analysis which in turn requires effective Public Health Knowledge Base (PHKB) and a strong efficient query engine. Ontologies enhance the performance of the retrieval system and achieve application interoperability. The current research aims at building PHKB through ontology based cross linked domain integration. It designs a dynamic GeoSPARQL query building from simple form based query composition. The spatial semantic multi criteria query engine is developed by identifying all possible query patterns considering the ontology elements and multi criteria from cross linked application domains. The research has adopted OGC, W3C, WHO and mHealth standards.
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35
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Hwang KH, Lee H, Koh G, Willrett D, Rubin DL. Building and Querying RDF/OWL Database of Semantically Annotated Nuclear Medicine Images. J Digit Imaging 2018; 30:4-10. [PMID: 27785632 DOI: 10.1007/s10278-016-9916-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
As the use of positron emission tomography-computed tomography (PET-CT) has increased rapidly, there is a need to retrieve relevant medical images that can assist image interpretation. However, the images themselves lack the explicit information needed for query. We constructed a semantically structured database of nuclear medicine images using the Annotation and Image Markup (AIM) format and evaluated the ability the AIM annotations to improve image search. We created AIM annotation templates specific to the nuclear medicine domain and used them to annotate 100 nuclear medicine PET-CT studies in AIM format using controlled vocabulary. We evaluated image retrieval from 20 specific clinical queries. As the gold standard, two nuclear medicine physicians manually retrieved the relevant images from the image database using free text search of radiology reports for the same queries. We compared query results with the manually retrieved results obtained by the physicians. The query performance indicated a 98 % recall for simple queries and a 89 % recall for complex queries. In total, the queries provided 95 % (75 of 79 images) recall, 100 % precision, and an F1 score of 0.97 for the 20 clinical queries. Three of the four images missed by the queries required reasoning for successful retrieval. Nuclear medicine images augmented using semantic annotations in AIM enabled high recall and precision for simple queries, helping physicians to retrieve the relevant images. Further study using a larger data set and the implementation of an inference engine may improve query results for more complex queries.
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Affiliation(s)
- Kyung Hoon Hwang
- Department of Nuclear Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Haejun Lee
- Department of Nuclear Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Geon Koh
- Department of Nuclear Medicine, Gachon University Gil Medical Center, Incheon, South Korea
| | - Debra Willrett
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Daniel L Rubin
- Department of Radiology, Stanford University, Stanford, CA, USA. .,Department of Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, USA.
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Lablans M, Kadioglu D, Muscholl M, Ückert F. Exploiting Distributed, Heterogeneous and Sensitive Data Stocks while Maintaining the Owner’s Data Sovereignty. Methods Inf Med 2018. [PMID: 26196653 DOI: 10.3414/me14-01-0137] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
SummaryBackground: To achieve statistical significance in medical research, biological or data samples from several bio- or databanks often need to be complemented by those of other institutions. For that purpose, IT-based search services have been established to locate datasets matching a given set of criteria in databases distributed across several institutions. However, previous approaches require data owners to disclose information about their samples, raising a barrier for their participation in the network.Objective: To devise a method to search distributed databases for datasets matching a given set of criteria while fully maintaining their owner’s data sovereignty.Methods: As a modification to traditional federated search services, we propose the decentral search, which allows the data owner a high degree of control. Relevant data are loaded into local bridgeheads, each under their owner’s sovereignty. Researchers can formulate criteria sets along with a project proposal using a central search broker, which then notifies the bridgeheads. The criteria are, however, treated as an inquiry rather than a query: Instead of responding with results, bridgeheads notify their owner and wait for his/her decision regarding whether and what to answer based on the criteria set, the matching datasets and the specific project proposal. Without the owner’s explicit consent, no data leaves his/ her institution.Results: The decentral search has been deployed in one of the six German Centers for Health Research, comprised of eleven university hospitals. In the process, compliance with German data protection regulations has been confirmed. The decentral search also marks the centerpiece of an open source registry software toolbox aiming to build a national registry of rare diseases in Germany.Conclusions: While the sacrifice of real-time answers impairs some use-cases, it leads to several beneficial side effects: improved data protection due to data parsimony, tolerance for incomplete data schema mappings and flexibility with regard to patient consent. Most importantly, as no datasets ever leave their institution, owners can reject projects without facing potential peer pressure. By its lower barrier for participation, a decentral search service is likely to attract a larger number of partners and to bring a researcher into contact with the right potential partners.
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Affiliation(s)
- M Lablans
- Martin Lablans, University Medical Center Mainz, Obere Zahlbacher Straße 69, 55131 Mainz, Germany, E-mail:
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Johnson T, Liebner D, Chen JL. Opportunities for Patient Matching Algorithms to Improve Patient Care in Oncology. JCO Clin Cancer Inform 2017; 1:1-8. [PMID: 30657369 DOI: 10.1200/cci.16.00042] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
| | - David Liebner
- All authors: The Ohio State University, Columbus, OH
| | - James L Chen
- All authors: The Ohio State University, Columbus, OH
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Evaluating the granularity balance of hierarchical relationships within large biomedical terminologies towards quality improvement. J Biomed Inform 2017; 75:129-137. [PMID: 28987379 DOI: 10.1016/j.jbi.2017.10.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 09/15/2017] [Accepted: 10/02/2017] [Indexed: 11/21/2022]
Abstract
Organizing the descendants of a concept under a particular semantic relationship may be rather arbitrarily carried out during the manual creation processes of large biomedical terminologies, resulting in imbalances in relationship granularity. This work aims to propose scalable models towards systematically evaluating the granularity balance of semantic relationships. We first utilize "parallel concepts set (PCS)" and two features (the length and the strength) of the paths between PCSs to design the general evaluation models, based on which we propose eight concrete evaluation models generated by two specific types of PCSs: single concept set and symmetric concepts set. We then apply those concrete models to the IS-A relationship in FMA and SNOMED CT's Body Structure subset, as well as to the Part-Of relationship in FMA. Moreover, without loss of generality, we conduct two additional rounds of applications on the Part-Of relationship after removing length redundancies and strength redundancies sequentially. At last, we perform automatic evaluation on the imbalances detected after the final round for identifying missing concepts, misaligned relations and inconsistencies. For the IS-A relationship, 34 missing concepts, 80 misalignments and 18 redundancies in FMA as well as 28 missing concepts, 114 misalignments and 1 redundancy in SNOMED CT were uncovered. In addition, 6,801 instances of imbalances for the Part-Of relationship in FMA were also identified, including 3,246 redundancies. After removing those redundancies from FMA, the total number of Part-Of imbalances was dramatically reduced to 327, including 51 missing concepts, 294 misaligned relations, and 36 inconsistencies. Manual curation performed by the FMA project leader confirmed the effectiveness of our method in identifying curation errors. In conclusion, the granularity balance of hierarchical semantic relationship is a valuable property to check for ontology quality assurance, and the scalable evaluation models proposed in this study are effective in fulfilling this task, especially in auditing relationships with sub-hierarchies, such as the seldom evaluated Part-Of relationship.
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Diepenbroek M, Schindler U, Huber R, Pesant S, Stocker M, Felden J, Buss M, Weinrebe M. Terminology supported archiving and publication of environmental science data in PANGAEA. J Biotechnol 2017; 261:177-186. [PMID: 28743591 DOI: 10.1016/j.jbiotec.2017.07.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 07/17/2017] [Accepted: 07/19/2017] [Indexed: 02/04/2023]
Abstract
Exemplified on the information system PANGAEA, we describe the application of terminologies for archiving and publishing environmental science data. A terminology catalogue (TC) was embedded into the system, with interfaces allowing to replicate and to manually work on terminologies. For data ingest and archiving, we show how the TC can improve structuring and harmonizing lineage and content descriptions of data sets. Key is the conceptualization of measurement and observation types (parameters) and methods, for which we have implemented a basic syntax and rule set. For data access and dissemination, we have improved findability of data through enrichment of metadata with TC terms. Semantic annotations, e.g. adding term concepts (including synonyms and hierarchies) or mapped terms of different terminologies, facilitate comprehensive data retrievals. The PANGAEA thesaurus of classifying terms, which is part of the TC is used as an umbrella vocabulary that links the various domains and allows drill downs and side drills with various facets. Furthermore, we describe how TC terms can be linked to nominal data values. This improves data harmonization and facilitates structural transformation of heterogeneous data sets to a common schema. Technical developments are complemented by work on the metadata content. Over the last 20 years, more than 100 new parameters have been defined on average per week. Recently, PANGAEA has increasingly been submitting new terms to various terminology services. Matching terms from terminology services with our parameter or method strings is supported programmatically. However, the process ultimately needs manual input by domain experts. The quality of terminology services is an additional limiting factor, and varies with respect to content, editorial, interoperability, and sustainability. Good quality terminology services are the building blocks for the conceptualization of parameters and methods. In our view, they are essential for data interoperability and arguably the most difficult hurdle for data integration. In summary, the application of terminologies has a mutual positive effect for terminology services and information systems such as PANGAEA. On both sides, the application of terminologies improves content, reliability and interoperability.
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Affiliation(s)
- Michael Diepenbroek
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Uwe Schindler
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Robert Huber
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Stéphane Pesant
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Markus Stocker
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Janine Felden
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Melanie Buss
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany.
| | - Matthias Weinrebe
- PANGAEA Data Publisher for Earth & Environmental Science, MARUM Center for Marine Environmental Sciences, University of Bremen, P.O. Box 33 04 40, 28334 Bremen, Germany
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Pfaff M, Krcmar H. A web-based system architecture for ontology-based data integration in the domain of IT benchmarking. ENTERP INF SYST-UK 2017. [DOI: 10.1080/17517575.2017.1329552] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Matthias Pfaff
- Department of Business Model and Service Engineering, fortiss GmbH, An-Institut Technische Universität München (TUM), München, Germany
- Department of Informatics, Technische Universität München (TUM), München, Germany
| | - Helmut Krcmar
- Department of Informatics, Technische Universität München (TUM), München, Germany
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Cui L, Huang Y, Tao S, Lhatoo SD, Zhang GQ. ODaCCI: Ontology-guided Data Curation for Multisite Clinical Research Data Integration in the NINDS Center for SUDEP Research. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:441-450. [PMID: 28269839 PMCID: PMC5333343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Sudden Unexpected Death in Epilepsy (SUDEP) is the leading mode of epilepsy-related death. The Center for SUDEP Research (CSR) is an NINDS-funded Center Without Wall's initiative aimed at prospectively creating a comprehensive clinical research resource for SUDEP. This resource consists of a growing set of data and biological samples of a statistically significant cohort of patients at an elevated risk, best represented by the Epilepsy Monitoring Unit (EMU) patient population. The Informatics and Data Analytics Core (IDAC) of CSR has developed a state-of-the- art informatics infrastructure, to integrate patient data captured in multiple EMU's at a greatly accelerated pace. Data quality assurance is a priority of IDAC. This paper reports our approach, Ontology-guided Data Curation for Multisite Clinical Research Data Integration (ODaCCI), to address the challenging task of centralized data curation while new data is continuously generated and integrated from distributed sites. ODaCCI leverages the Epilepsy and Seizure Ontology not only for upstream data capture, but also for supporting a range of quality assurance tasks such as data quality monitoring, data update, and data reports. Between October 2014 and February 2016, ODaCCI has integrated phenotypic and electroencephalogram signal data of 629 patients from 7 clinical sites, while supporting continuous and asynchronous data quality enhancement overtime.
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Affiliation(s)
- Licong Cui
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY; Center for SUDEP Research (NINDS-funded Center Without Walls for Collaborative Research in the Epilepsies), Cleveland, OH
| | - Yan Huang
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY; Center for SUDEP Research (NINDS-funded Center Without Walls for Collaborative Research in the Epilepsies), Cleveland, OH
| | - Shiqiang Tao
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY
| | - Samden D Lhatoo
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Department of Computer Science, University of Kentucky, Lexington, KY
| | - Guo-Qiang Zhang
- Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Institute of Biomedical Informatics, University of Kentucky, Lexington, KY; Institute of Biomedical Informatics, University of Kentucky, Lexington, KY
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Rutzner S, Fietkau R, Ganslandt T, Prokosch HU, Lubgan D. Electronic Support for Retrospective Analysis in the Field of Radiation Oncology: Proof of Principle Using an Example of Fractionated Stereotactic Radiotherapy of 251 Meningioma Patients. Front Oncol 2017; 7:16. [PMID: 28232905 PMCID: PMC5298960 DOI: 10.3389/fonc.2017.00016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 01/24/2017] [Indexed: 01/18/2023] Open
Abstract
Introduction The purpose of this study is to verify the possible benefit of a clinical data warehouse (DWH) for retrospective analysis in the field of radiation oncology. Material and methods We manually and electronically (using DWH) evaluated demographic, radiotherapy, and outcome data from 251 meningioma patients, who were irradiated from January 2002 to January 2015 at the Department of Radiation Oncology of the Erlangen University Hospital. Furthermore, we linked the Oncology Information System (OIS) MOSAIQ® to the DWH in order to gain access to irradiation data. We compared the manual and electronic data retrieval method in terms of congruence of data, corresponding time, and personal requirements (physician, physicist, scientific associate). Results The electronically supported data retrieval (DWH) showed an average of 93.9% correct data and significantly (p = 0.009) better result compared to manual data retrieval (91.2%). Utilizing a DWH enables the user to replace large amounts of manual activities (668 h), offers the ability to significantly reduce data collection time and labor demand (35 h), while simultaneously improving data quality. In our case, work time for manually data retrieval was 637 h for the scientific assistant, 26 h for the medical physicist, and 5 h for the physician (total 668 h). Conclusion Our study shows that a DWH is particularly useful for retrospective analysis in the radiation oncology field. Routine clinical data for a large patient group can be provided ready for analysis to the scientist and data collection time can be significantly reduced. Furthermore, linking multiple data sources in a DWH offers the ability to improve data quality for retrospective analysis, and future research can be simplified.
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Affiliation(s)
- Sandra Rutzner
- Department of Radiation Oncology, Erlangen University Hospital , Erlangen , Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Erlangen University Hospital , Erlangen , Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-University of Erlangen-Nuremberg , Erlangen , Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-University of Erlangen-Nuremberg , Erlangen , Germany
| | - Dorota Lubgan
- Department of Radiation Oncology, Erlangen University Hospital , Erlangen , Germany
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Abstract
OBJECTIVES To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. METHOD A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. RESULTS Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations. CONCLUSIONS A major trend in the 2015 publications is the analysis of observational, "nonexperimental" information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.
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Affiliation(s)
- C Daniel
- Christel Daniel, MD, PhD, INSERM UMRS 1142 - WIND-DSI, - Assistance Publique - Hôpitaux de Paris, 05 rue Santerre, 75 012 Paris, France, Tel: +33 1 48 04 20 29, E-mail: christel.daniel@ aphp.fr
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Shen F, Lee Y. Knowledge Discovery from Biomedical Ontologies in Cross Domains. PLoS One 2016; 11:e0160005. [PMID: 27548262 PMCID: PMC4993478 DOI: 10.1371/journal.pone.0160005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 07/12/2016] [Indexed: 01/19/2023] Open
Abstract
In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.
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Affiliation(s)
- Feichen Shen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Yugyung Lee
- School of Computing and Engineering, University of Missouri - Kansas City, Kansas City, Missouri, United States of America
- * E-mail:
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Shen F, Liu H, Sohn S, Larson DW, Lee Y. Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery. INTELLIGENT INFORMATION MANAGEMENT 2016; 8:66-85. [PMID: 28983419 PMCID: PMC5626454 DOI: 10.4236/iim.2016.83006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.
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Affiliation(s)
- Feichen Shen
- CSEE Department, University of Missouri, Kansas City, MO, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Sunghwan Sohn
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - David W Larson
- Department of Surgery, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Yugyung Lee
- CSEE Department, University of Missouri, Kansas City, MO, USA
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Lablans M, Kadioglu D, Mate S, Leb I, Prokosch HU, Ückert F. Strategien zur Vernetzung von Biobanken. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2016; 59:373-8. [DOI: 10.1007/s00103-015-2299-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Zusammenfassung
Hintergrund
Nicht selten benötigt ein medizinisches Forschungsvorhaben mehr biologisches Material, als in einer einzigen Biobank verfügbar ist. Daher unterstützt eine Vielzahl von Strategien das Auffinden potentieller Forschungspartner mit passenden Proben, auch ohne dass diese zuvor in einer zentralisierten Sammlung zusammengeführt werden müssen.
Ziel
Der vorliegende Beitrag beschreibt die Klassifizierung verschiedener Strategien zur Vernetzung von Biomaterialbanken, speziell zur Probensuche, sowie eine IT-Infrastruktur, die diese Ansätze kombiniert.
Material und Methoden
Bestehende Strategien lassen sich nach drei Kriterien klassifizieren: a) Granularität der Probendaten: grobe Daten auf Bankebene (Katalog) vs. feingranulare Daten auf Probenebene, b) Speicherort der Probendaten: zentrale (zentraler Suchdienst) vs. dezentrale Datenhaltung (föderierte Suchdienste) und c) Automatisierungsgrad: automatisch (abfragebasiert, föderierter Suchdienst) vs. halbautomatisch (anfragebasiert, dezentrale Suche). Alle genannten Suchdienste setzen eine Datenintegration voraus; dabei helfen Metadaten bei der Überwindung semantischer Heterogenität.
Ergebnisse
Der „Common Service IT“ in BBMRI-ERIC („Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium“) vereint einen Katalog, die dezentrale Suche und Metadaten in einer integrierten Plattform, um Forschern vielseitige Werkzeuge zur Suche nach passendem Probenmaterial zu geben und bei den Biobankern gleichzeitig ein hohes Maß an Datenhoheit zu bewahren.
Diskussion
Trotz ihrer Unterschiede schließen sich die vorgestellten Strategien zur Vernetzung von Biomaterialbanken gegenseitig nicht aus. Vielmehr lassen sie sich in gemeinsamen Forschungsinfrastrukturen sinnvoll ergänzen und sie können sogar voneinander profitieren.
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Affiliation(s)
- Martin Lablans
- Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland.
| | - Dennis Kadioglu
- Institut für medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Universitätsmedizin Mainz, 55101, Mainz, Deutschland
| | - Sebastian Mate
- Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland
| | - Ines Leb
- Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland
| | - Hans-Ulrich Prokosch
- Lehrstuhl für Medizinische Informatik, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland
| | - Frank Ückert
- Medizinische Informatik in der Translationalen Onkologie, Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland
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Cui L. COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:456-465. [PMID: 26958178 PMCID: PMC4765676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance qualities of biomedical ontologies by performing cross-ontology examination.
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Affiliation(s)
- Licong Cui
- Department of EECS, Case Western Reserve University, Cleveland, OH Division of Medical Informatics, Case Western Reserve University, Cleveland, OH
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