ArticleCurrent protocols in human genetics2019
Using Electronic Health Records To Generate Phenotypes For Research.
Article in Current protocols in human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 2 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
75 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review.Reviews in cardiovascular medicine · 2025Pooled it
- Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Multi-ancestry genome-wide association study in all of Us for primary open-angle glaucoma.Scientific reports · 2026Article
- OMICmAge quantifies biological age by integrating multi-omics with electronic medical records.Nature aging · 2026Article
- Recent Advances in AI and GenAI for Health Informatics.Healthcare (Basel, Switzerland) · 2026Review
- Use of machine learning for chronic disease case identification in primary care settings: A scoping review of methods, validation, and clinical relevance.The Journal of international medical research · 2026Article
- A scoping review of models to identify transgender patients in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Multi-Ancestry Genome-Wide Association Study in All of Us for Primary Open- Angle Glaucoma.Research square · 2025Article
- Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Phenome-wide association study identifies multiple traits associated with a polygenic risk score for colorectal cancer.Human genomics · 2025Article
- Label efficient phenotyping for Long COVID using electronic health records.NPJ digital medicine · 2025Article
- An evaluation and comparison of traumatic brain injury data sources within the VA Million Veteran Program: A descriptive epidemiological study.Journal of psychiatric research · 2025Article
- Machine-Learned Codes from EHR Data Predict Hard Outcomes Better than Human-Assigned ICD Codes.Machine learning and knowledge extraction · 2025Article
- SepsisJournal of medical microbiology · 2025Review
- Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making.PloS one · 2025Article
- With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de-identified electronic health record data for research.Clinical and translational science · 2025Article
- Pharmacogenetics: Opportunities for theAnnual review of pharmacology and toxicology · 2025Review
- Multisource representation learning for pediatric knowledge extraction from electronic health records.NPJ digital medicine · 2024Article
15 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
2 authors.
Funding
Abstract
Electronic health records contain patient-level data collected during and for clinical care. Data within the electronic health record include diagnostic billing codes, procedure codes, vital signs, laboratory test results, clinical imaging, and physician notes. With repeated clinic visits, these data are longitudinal, providing important information on disease development, progression, and response to treatment or intervention strategies. The near universal adoption of electronic health records nationally has the potential to provide population-scale real-world clinical data accessible for biomedical research, including genetic association studies. For this research potential to be realized, high-quality research-grade variables must be extracted from these clinical data warehouses. We describe here common and emerging electronic phenotyping approaches applied to electronic health records, as well as current limitations of both the approaches and the biases associated with these clinically collected data that impact their use in research. © 2018 by John Wiley & Sons, Inc.
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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.