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Schiffer-Kane K, Liu C, Callahan TJ, Ta C, Nestor JG, Weng C. Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation. J Biomed Inform 2024; 155:104659. [PMID: 38777085 PMCID: PMC11181468 DOI: 10.1016/j.jbi.2024.104659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/11/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
OBJECTIVE This study aims to promote interoperability in precision medicine and translational research by aligning the Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models. Phenopackets is an expert knowledge-driven schema designed to facilitate the storage and exchange of multimodal patient data, and support downstream analysis. The first goal of this paper is to explore model alignment by characterizing the common data models using a newly developed data transformation process and evaluation method. Second, using OMOP normalized clinical data, we evaluate the mapping of real-world patient data to Phenopackets. We evaluate the suitability of Phenopackets as a patient data representation for real-world clinical cases. METHODS We identified mappings between OMOP and Phenopackets and applied them to a real patient dataset to assess the transformation's success. We analyzed gaps between the models and identified key considerations for transforming data between them. Further, to improve ambiguous alignment, we incorporated Unified Medical Language System (UMLS) semantic type-based filtering to direct individual concepts to their most appropriate domain and conducted a domain-expert evaluation of the mapping's clinical utility. RESULTS The OMOP to Phenopacket transformation pipeline was executed for 1,000 Alzheimer's disease patients and successfully mapped all required entities. However, due to missing values in OMOP for required Phenopacket attributes, 10.2 % of records were lost. The use of UMLS-semantic type filtering for ambiguous alignment of individual concepts resulted in 96 % agreement with clinical thinking, increased from 68 % when mapping exclusively by domain correspondence. CONCLUSION This study presents a pipeline to transform data from OMOP to Phenopackets. We identified considerations for the transformation to ensure data quality, handling restrictions for successful Phenopacket validation and discrepant data formats. We identified unmappable Phenopacket attributes that focus on specialty use cases, such as genomics or oncology, which OMOP does not currently support. We introduce UMLS semantic type filtering to resolve ambiguous alignment to Phenopacket entities to be most appropriate for real-world interpretation. We provide a systematic approach to align OMOP and Phenopackets schemas. Our work facilitates future use of Phenopackets in clinical applications by addressing key barriers to interoperability when deriving a Phenopacket from real-world patient data.
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Affiliation(s)
- Kayla Schiffer-Kane
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Casey Ta
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Jordan G Nestor
- Department of Medicine, Division of Nephrology, Columbia University Irving Medical Center, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
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Koonce TY, Giuse DA, Williams AM, Blasingame MN, Krump PA, Su J, Giuse NB. Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition. JMIR Med Inform 2024; 12:e53516. [PMID: 38289670 PMCID: PMC10865202 DOI: 10.2196/53516] [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: 10/10/2023] [Revised: 12/15/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.
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Affiliation(s)
- Taneya Y Koonce
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dario A Giuse
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Annette M Williams
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Mallory N Blasingame
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Poppy A Krump
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jing Su
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Nunzia B Giuse
- Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
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Umberfield EE, Ball Dunlap PA, Harris MR. The case for expressing nursing theories using ontologies. J Am Med Inform Assoc 2023; 30:1865-1867. [PMID: 37308323 PMCID: PMC10586024 DOI: 10.1093/jamia/ocad095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/17/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023] Open
Abstract
Nursing and informatics share a common strength in their use of structured representations of domains, specifically the underlying notion of 'things' (ie, concepts, constructs, or named entities) and the relationships among those things. Accurate representation of nursing knowledge in machine-interpretable formats is a necessary next step for leveraging contemporary technologies. Expressing validated nursing theories in ontologies, and in particular formal ontologies, would serve not only nursing, but also investigators from other domains, clinical information system developers, and the users of advanced technologies such as artificial intelligence that seek to learn from the real-world data and evidence generated by nurses and others. Such efforts will enable sharing knowledge and conceptualizations about phenomena across the domains of nursing and generating, testing, revising, and providing theoretically-based perspectives when leveraging contemporary technologies. Nursing is well situated for this work, leveraging intentional and focused collaborations among nurse informaticists, scientists, and theorists.
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Affiliation(s)
- Elizabeth E Umberfield
- Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, Minnesota, USA
- Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Patricia A Ball Dunlap
- Center for Digital Health, Mayo Clinic, Rochester, Minnesota, USA
- School of Nursing, University of Maryland, Baltimore, Baltimore, Maryland, USA
| | - Marcelline R Harris
- Emeritus, School of Nursing, University of Michigan, Ann Arbor, Michigan, USA
- Independent Consultant, Seattle, Washington, USA
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Wilson LJ, Kiffer FC, Berrios DC, Bryce-Atkinson A, Costes SV, Gevaert O, Matarèse BFE, Miller J, Mukherjee P, Peach K, Schofield PN, Slater LT, Langen B. Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium. Int J Radiat Biol 2023:1-10. [PMID: 36735963 DOI: 10.1080/09553002.2023.2173823] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcome critical limitations on how those large-scale data sets are processed, analyzed, and interpreted. The 67th Annual Meeting of the Radiation Research Society featured a symposium on MI approaches to highlight recent advancements in the radiation sciences and their clinical applications. This article summarizes three of those presentations regarding recent developments for metadata processing and ontological formalization, data mining for radiation outcomes in pediatric oncology, and imaging in lung cancer.
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Affiliation(s)
- Lydia J Wilson
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Frederico C Kiffer
- Department of Anesthesia and Critical Care Medicine, The Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
| | | | - Abigail Bryce-Atkinson
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | | | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Bruno F E Matarèse
- The Cavendish Laboratory, University of Cambridge, Cambridge, UK
- Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Jack Miller
- NASA Ames Research Center, Moffett Field, CA, USA
- KBR, NASA Ames Research Center, Moffett Field, CA, USA
| | - Pritam Mukherjee
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford, CA, USA
- Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD, USA
| | - Kristen Peach
- Department of Bionetics, NASA Ames Research Center, Moffett Field, CA, USA
| | - Paul N Schofield
- Department of Physiology Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Luke T Slater
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, UK
- MRC Health Data Research UK (HDR UK), Midlands, UK
| | - Britta Langen
- Department of Radiation Oncology, Section of Molecular Radiation Biology, UT Southwestern Medical Center, Dallas, TX, USA
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Köhler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM, Callahan TJ, Chute CG, Est JL, Galer PD, Ganesan S, Griese M, Haimel M, Pazmandi J, Hanauer M, Harris NL, Hartnett M, Hastreiter M, Hauck F, He Y, Jeske T, Kearney H, Kindle G, Klein C, Knoflach K, Krause R, Lagorce D, McMurry JA, Miller JA, Munoz-Torres M, Peters RL, Rapp CK, Rath AM, Rind SA, Rosenberg A, Segal MM, Seidel MG, Smedley D, Talmy T, Thomas Y, Wiafe SA, Xian J, Yüksel Z, Helbig I, Mungall CJ, Haendel MA, Robinson PN. The Human Phenotype Ontology in 2021. Nucleic Acids Res 2021; 49:D1207-D1217. [PMID: 33264411 PMCID: PMC7778952 DOI: 10.1093/nar/gkaa1043] [Citation(s) in RCA: 541] [Impact Index Per Article: 180.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/11/2020] [Accepted: 11/16/2020] [Indexed: 12/21/2022] Open
Abstract
The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.
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Affiliation(s)
| | - Michael Gargano
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Nicolas Matentzoglu
- Monarch Initiative
- Semanticly Ltd, London, UK
- European Bioinformatics Institute (EMBL-EBI)
| | - Leigh C Carmody
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - David Lewis-Smith
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Clinical Neurosciences, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Nicole A Vasilevsky
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
| | | | - Ganna Balagura
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, University of Genoa, Genoa, Italy
- Pediatric Neurology and Muscular Diseases Unit, IRCCS ‘G. Gaslini’ Institute, Genoa, Italy
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies, King Edward memorial Hospital, Perth, Australia
- Telethon Kids Institute and the Division of Paediatrics, Faculty of Helath and Medical Sciences, University of Western Australia, Perth, Australia
| | - Amy M Brower
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Colorado, USA
| | | | - Johanna L Est
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthias Griese
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Matthias Haimel
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Julia Pazmandi
- Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases, Vienna, Austria
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Marc Hanauer
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Nomi L Harris
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Michael J Hartnett
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Maximilian Hastreiter
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fabian Hauck
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- German Centre for Infection Research (DZIF), Munich, Germany
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Tim Jeske
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Hugh Kearney
- FutureNeuro, SFI Research Centre for Chronic and Rare Neurological Diseases, Ireland
| | - Gerhard Kindle
- Institute for Immunodeficiency, Center for Chronic Immunodeficiency (CCI). Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
- Centre for Biobanking FREEZE, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Christoph Klein
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Katrin Knoflach
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Roland Krause
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - David Lagorce
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Julie A McMurry
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Jillian A Miller
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Monica C Munoz-Torres
- Monarch Initiative
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Rebecca L Peters
- American College of Medical Genetics and Genomics (ACMG), Bethesda, MD, USA
| | - Christina K Rapp
- Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
- Ludwig-Maximilians University, German Center for Lung Research (DZL), Munich, Germany
| | - Ana M Rath
- INSERM, US14––Orphanet, Plateforme Maladies Rares, Paris, France
| | - Shahmir A Rind
- WA Register of Developmental Anomalies
- Curtin University, Western Australia, Australia
| | - Avi Z Rosenberg
- Division of Kidney-Urologic Pathology, Johns Hopkins University, Baltimore, MD 21205, USA
| | | | - Markus G Seidel
- Research Unit for Pediatric Hematology and Immunology, Division of Pediatric Hemato-Oncology, Department of Pediatrics and Adolescent Medicine, Medical University of Graz, Graz, Austria
| | - Damian Smedley
- The William Harvey Research Institute, Charterhouse Square Barts and the London School of Medicine and Dentistry Queen Mary University of London, London EC1M 6BQ, UK
| | - Tomer Talmy
- Genomic Research Department, Emedgene Technologies, Tel Aviv, Israel
- Faculty of Medicine, Hebrew University Hadassah Medical School, Jerusalem, Israel
| | - Yarlalu Thomas
- West Australian Register of Developmental Anomalies, East Perth, WA, Australia
| | | | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, PA, USA
| | - Zafer Yüksel
- Human Genetics, Bioscientia GmbH, Ingelheim, Germany
| | - Ingo Helbig
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Christopher J Mungall
- Monarch Initiative
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley CA, USA
| | - Melissa A Haendel
- Monarch Initiative
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University
- Translational and Integrative Sciences Center, Department of Environmental and Molecular Toxicology, Oregon State University, OR, USA
| | - Peter N Robinson
- Monarch Initiative
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
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