ArticleScientific reports2023
Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The consensus-based CINEX guideline for reporting clinical information extraction studies.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Cardiovascular-Kidney-Metabolic Syndrome: Development of anJMIR diabetes · 2026Article
- Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping Algorithms.Research square · 2026Article
- Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping Algorithms.medRxiv : the preprint server for health sciences · 2026Article
- The Electronic Medical Records and Genomics study: Design and analytic framework for assessing the impact of genome-informed risk assessments.American journal of human genetics · 2026Article
- Development and validation of a multi-institutional electronic juvenile idiopathic arthritis phenotype.Seminars in arthritis and rheumatism · 2026Article
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Advancing delirium detection through the Open Health Natural Language Processing Consortium and the Evolve to Next-Gen Accrual to Clinical Trials Network.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study.JAMIA open · 2025Review
- Guideline-based strategies to identify severe cytokine release syndrome in COVID-19 and cancer immunotherapy using large-scale electronic health records.Frontiers in digital health · 2025Article
- Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Large language models facilitate the generation of electronic health record phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms.medRxiv : the preprint server for health sciences · 2024Article
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Authors and funding
39 authors.
Funding
Abstract
The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.
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