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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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Casiraghi E, Wong R, Hall M, Coleman B, Notaro M, Evans MD, Tronieri JS, Blau H, Laraway B, Callahan TJ, Chan LE, Bramante CT, Buse JB, Moffitt RA, Stürmer T, Johnson SG, Raymond Shao Y, Reese J, Robinson PN, Paccanaro A, Valentini G, Huling JD, Wilkins KJ. A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. J Biomed Inform 2023; 139:104295. [PMID: 36716983 PMCID: PMC10683778 DOI: 10.1016/j.jbi.2023.104295] [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: 06/02/2022] [Revised: 01/16/2023] [Accepted: 01/21/2023] [Indexed: 02/01/2023]
Abstract
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.
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Affiliation(s)
- Elena Casiraghi
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Margaret Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Marco Notaro
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy
| | - Michael D Evans
- Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jena S Tronieri
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, USA
| | - Bryan Laraway
- University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | | | - Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, USA
| | - Carolyn T Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - John B Buse
- NC Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Endocrinology, Department of Medicine, University of North Carolina School of Medicine, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Yu Raymond Shao
- Harvard-MIT Division of Health Sciences and Technology (HST), 260 Longwood Ave, Boston, USA; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Alberto Paccanaro
- School of Applied Mathematics (EMAp), Fundação Getúlio Vargas, Rio de Janeiro, Brazil; Department of Computer Science, Royal Holloway, University of London, Egham, UK
| | - Giorgio Valentini
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy
| | - Jared D Huling
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kenneth J Wilkins
- Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
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