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He T, Belouali A, Patricoski J, Lehmann H, Ball R, Anagnostou V, Kreimeyer K, Botsis T. Trends and opportunities in computable clinical phenotyping: A scoping review. J Biomed Inform 2023; 140:104335. [PMID: 36933631 DOI: 10.1016/j.jbi.2023.104335] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023]
Abstract
Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.
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Affiliation(s)
- Ting He
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Anas Belouali
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jessica Patricoski
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harold Lehmann
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Ball
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD, USA
| | - Valsamo Anagnostou
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kory Kreimeyer
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Taxiarchis Botsis
- Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Belenkaya R, Gurley MJ, Golozar A, Dymshyts D, Miller RT, Williams AE, Ratwani S, Siapos A, Korsik V, Warner J, Campbell WS, Rivera D, Banokina T, Modina E, Bethusamy S, Stewart HM, Patel M, Chen R, Falconer T, Park RW, You SC, Jeon H, Shin SJ, Reich C. Extending the OMOP Common Data Model and Standardized Vocabularies to Support Observational Cancer Research. JCO Clin Cancer Inform 2021; 5:12-20. [PMID: 33411620 PMCID: PMC8140810 DOI: 10.1200/cci.20.00079] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
| | - Michael J Gurley
- Clinical and Translational Sciences Institute, Northwestern University, Evanston, IL
| | | | | | - Robert T Miller
- Tufts Clinical and Translational Science Institute, Boston, MA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA
| | | | | | | | | | | | | | | | | | | | | | | | - Ruijun Chen
- Department of Biomedical Informatics, Columbia University, New York City, NY
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York City, NY
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Hokyun Jeon
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Soe Jeong Shin
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
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