ArticleJournal of the American Medical Informatics Association : JAMIA2023
Machine learning approaches for electronic health records phenotyping: a methodical review.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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Who cites it
78 citing papers in PubMed.
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Artificial Intelligence for the Prediction of Preeclampsia: Current Evidence, Comparison with Conventional Screening Models, and Future Perspectives.Diagnostics (Basel, Switzerland) · 2026Review
- Uncoupling Type I Interferon Benefits From Inflammatory Toxicity: Transformer-Prioritized Precision Agonists for Potent and Safer Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study.Circulation · 2026Article
- A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health records.medRxiv : the preprint server for health sciences · 2026Article
- Patients' and Providers' Attitudes Toward Artificial Intelligence and Electronic Health Record Use in Deep Phenotyping and Rare-Disease Screening: An Empty Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.NPJ digital medicine · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Early attention deficit hyperactivity disorder prediction from longitudinal electronic health records.Nature. Mental health · 2026Article
- Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes Using a Local Large Language Model.Translational vision science & technology · 2026Article
- Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- An operational target trial emulation framework for causal inference using electronic health record data.NPJ digital medicine · 2026Review
- Temporal Learning with Dynamic Range (TLDR) for modeling recurrent exposure and treatment outcomes.Scientific reports · 2026Article
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- Article
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
18 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAccurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an alternative to improve scalability across phenotypes and healthcare settings. This study evaluates ML-based phenotyping with respect to (1) the data sources used, (2) the phenotypes considered, (3) the methods applied, and (4) the reporting and evaluation methods used. MATERIALS AND
methodsWe searched PubMed and Web of Science for articles published between 2018 and 2022. After screening 850 articles, we recorded 37 variables on 100 studies.
resultsMost studies utilized data from a single institution and included information in clinical notes. Although chronic conditions were most commonly considered, ML also enabled the characterization of nuanced phenotypes such as social determinants of health. Supervised deep learning was the most popular ML paradigm, while semi-supervised and weakly supervised learning were applied to expedite algorithm development and unsupervised learning to facilitate phenotype discovery. ML approaches did not uniformly outperform rule-based algorithms, but deep learning offered a marginal improvement over traditional ML for many conditions. DISCUSSION: Despite the progress in ML-based phenotyping, most articles focused on binary phenotypes and few articles evaluated external validity or used multi-institution data. Study settings were infrequently reported and analytic code was rarely released.
conclusionContinued research in ML-based phenotyping is warranted, with emphasis on characterizing nuanced phenotypes, establishing reporting and evaluation standards, and developing methods to accommodate misclassified phenotypes due to algorithm errors in downstream applications.
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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.