ArticleNature protocols2019
High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).
Article in Nature protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis 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
100 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of idiopathic inflammatory myopathy research cohorts using international classification of disease (ICD) codes: A systematic review.Seminars in arthritis and rheumatism · 2025Pooled it
- Optimizing metabolic health through deep-phenotyping-based personalized nutrition.Nature reviews. Endocrinology · 2026Review
- Intelligent Framework for Adverse Drug Event Identification Using Large Language Models and Retrieval-Augmented Generation: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- Using electronic health record phenotyping to guide extraction of markers of transition to adulthood in young adults with severe chronic illness: A proposed conceptual framework.Nursing outlook · 2026Article
- Phenotyping antidepressant treatment response with deep learning in electronic health records.Translational psychiatry · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.Communications medicine · 2026Article
- Antidiabetic Drug Associations With Heart Failure Outcomes: Real-World Evidence Study Using Electronic Health Records.JMIR diabetes · 2026Article
- Leveraging electronic health records to examine differential clinical outcomes in people with Alzheimer's disease.Communications medicine · 2026Article
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
- Detecting Pediatric Emergency Service Use for Suicide and Self-Harm: Multimodal Analysis of 3828 Encounters.JMIR mental health · 2026Article
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 2026Article
- When algorithms infer gender: revisiting computational phenotyping with electronic health records data.Biology of sex differences · 2025Review
- Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.Scientific reports · 2025Article
- Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model.medRxiv : the preprint server for health sciences · 2025Article
- Label efficient phenotyping for Long COVID using electronic health records.NPJ digital medicine · 2025Article
- A machine learning model using clinical notes to identify physician fatigue.Nature communications · 2025Article
- Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes.Journal of medical Internet research · 2025Article
40 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
28 authors.
Funding
Abstract
Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).
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.