ArticleGenome medicine2015
Extracting research-quality phenotypes from electronic health records to support precision medicine.
Article in Genome medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 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
152 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- LabWAS: Novel findings and study design recommendations from a meta-analysis of clinical labs in two independent biobanks.PLoS genetics · 2020Pooled it
- Phenome-wide association studies across large population cohorts support drug target validation.Nature communications · 2018Pooled it
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Introducing the TOP framework: a novel phenotyping solution for collaborative phenotype algorithm development and application.Journal of biomedical semantics · 2026Article
- DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.The annals of applied statistics · 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
- Comparative neurologic toxicity profiles of chemotherapy versus immune checkpoint inhibitors in melanoma: a propensity score-matched analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Article
- Analytic Choices Shape Genomic Risk Estimates from Electronic Health Records: Coronary Heart Disease in eMERGE IV.medRxiv : the preprint server for health sciences · 2026Article
- Racial and sex differences in acute pericarditis among emergency department chest pain patients: a retrospective cohort study.BMC cardiovascular disorders · 2026Article
- A scoping review of models to identify transgender patients in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Replication of COPD susceptibility loci in a large Chinese elderly population using a validated, multi-setting EHR phenotype.BMC pulmonary medicine · 2026Article
- When algorithms infer gender: revisiting computational phenotyping with electronic health records data.Biology of sex differences · 2025Review
- Assessing long-term pleiotropic effects of potential novel triglyceride-lowering medications using variants identified by Mendelian randomization.medRxiv : the preprint server for health sciences · 2025Article
- Integrative Proteome- and Phenome-Wide Assessment Uncovers Causal Protein Drivers and Drug Targets for Heterogeneous Kidney Diseases.medRxiv : the preprint server for health sciences · 2025Article
- RAB19, SERPINB9P1, and Pancreatitis in Patients Taking Azathioprine in Routine Clinical Practice: Genome and Transcriptome-Wide Association Studies.Clinical pharmacology and therapeutics · 2025Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
92 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
The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.
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.