ReviewJournal of the American Medical Informatics Association : JAMIA
A review of approaches to identifying patient phenotype cohorts using electronic health records.
Review in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 273 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
273 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review.JMIR medical informatics · 2024Pooled it
- Natural Language Processing Applications in the Clinical Neurosciences: A Machine Learning Augmented Systematic Review.Acta neurochirurgica. Supplement · 2022Pooled it
- Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews.International journal of medical informatics · 2021Pooled it
- An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities.Journal of neurodevelopmental disorders · 2022Trial
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- Review
- Identifying Hemophagocytic Lymphohistiocytosis and Describing Outcomes Using Computable Phenotypes: Retrospective Cohort Study.JMIR cancer · 2026Article
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 2026Article
- Automating clinical phenotyping using natural language processing.Communications medicine · 2026Article
- Association Between Isolated Regional Pain and Subsequent Fibromyalgia Documentation: A Retrospective Text-Mined Cohort Study.Journal of pain research · 2026Article
- Large-scale automated phenotyping of cardiac arrest and withdrawal of life-sustaining therapy using electronic health record data.Resuscitation · 2026Article
- TimeX: Phenotype Onset Extraction from Clinical Narratives.npj health systems · 2026Article
- When algorithms infer gender: revisiting computational phenotyping with electronic health records data.Biology of sex differences · 2025Review
- Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Using unsupervised machine learning methods to cluster cardio-metabolic profile of the middle-aged and elderly Chinese with general and central obesity.BMC cardiovascular disorders · 2025Article
- Robust comparative evaluation of 15 natural language processing algorithms to positively identify patients with inflammatory bowel disease from secondary care records.BMJ open gastroenterology · 2025Article
- Article
- Article
- A prototype ETL pipeline that uses HL7 FHIR RDF resources when deploying pure functions to enrich knowledge graph patient data.Journal of biomedical semantics · 2025Article
213 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveTo summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. MATERIALS AND
methodsWe performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included.
resultsNinety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. DISCUSSION: We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems.
conclusionsThere are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.