ArticleJournal of the American Medical Informatics Association : JAMIA2013
A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 71 citations in OpenAlex.
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- Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.PloS one · 2023Article
- Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study.JMIR dermatology · 2022Article
- Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.JAMA network open · 2022Article
- Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- Predictive Analytics for Care and Management of Patients With Acute Diseases: Deep Learning-Based Method to Predict Crucial Complication Phenotypes.Journal of medical Internet research · 2021Article
- Utility of a Computerized ICD-10 Algorithm to Identify Idiosyncratic Drug-Induced Liver Injury Cases in the Electronic Medical Record.Drug safety · 2020Article
- A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.Radiology · 2019Article
- Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification.BMC medical informatics and decision making · 2019Article
- The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development.Clinical pharmacology and therapeutics · 2018Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress.Yearbook of medical informatics · 2017Review
- A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury.Digestive diseases and sciences · 2017Article
- Automated identification of an aspirin-exacerbated respiratory disease cohort.The Journal of allergy and clinical immunology · 2017Article
Corrections and comments
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Authors and funding
17 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveTo describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI).
methodsWe analyzed types and causes of differences in DILI case definitions provided by two institutions-Columbia University and Mayo Clinic; harmonized two EHR phenotyping algorithms; and assessed the performance, measured by sensitivity, specificity, positive predictive value, and negative predictive value, of the resulting algorithm at three institutions except that sensitivity was measured only at Columbia University.
resultsAlthough these sites had the same case definition, their phenotyping methods differed by selection of liver injury diagnoses, inclusion of drugs cited in DILI cases, laboratory tests assessed, laboratory thresholds for liver injury, exclusion criteria, and approaches to validating phenotypes. We reached consensus on a DILI phenotyping algorithm and implemented it at three institutions. The algorithm was adapted locally to account for differences in populations and data access. Implementations collectively yielded 117 algorithm-selected cases and 23 confirmed true positive cases. DISCUSSION: Phenotyping for rare conditions benefits significantly from pooling data across institutions. Despite the heterogeneity of EHRs and varied algorithm implementations, we demonstrated the portability of this algorithm across three institutions. The performance of this algorithm for identifying DILI was comparable with other computerized approaches to identify adverse drug events.
conclusionsPhenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.