ReviewAnnual review of biomedical data science2021
Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS.
Review in Annual review of biomedical data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 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
147 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review to Summarize and Critically Appraise Existing Phenotype Libraries Using Electronic Health Records.Pharmacoepidemiology and drug safety · 2026Pooled it
- Clinical spectrum of Transthyretin amyloidogenic mutations among diverse population origins.Human genomics · 2024Pooled it
- Temporal windowing of recurrent sinusitis improves EHR-based immunodeficiency classification.Journal of human immunity · 2026Article
- Long-Term Multisystem Comorbidity Patterns in Acute Pancreatitis: Insights From Observational and Genetic Analysis.United European gastroenterology journal · 2026Observational
- Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.EBioMedicine · 2026Article
- Article
- Transcript-aware rare genetic variant association analyses of cardiopulmonary traits in participants from the All of Us Research Program.Nature communications · 2026Article
- Aortic Geometric Atlas: Centile-Based Reference Charts and Pathological Signatures Across the Adult Lifespan.medRxiv : the preprint server for health sciences · 2026Article
- Multi-Level Latent Variable Models for Coheritability Analysis in Electronic Health Records.Statistics in medicine · 2026Article
- DOMAIN-AWARE MATRIX COMPLETION FOR PHENOTYPE IMPUTATION USING ELECTRONIC HEALTH RECORD DATA WITH APPLICATIONS IN GENOMIC RESEARCH.The annals of applied statistics · 2026Article
- Pleiotropic shared heritability quantifies the shared genetic variance of common diseases.Nature genetics · 2026Article
- Faster CYP2A6 increases COPD and lung cancer risk by increasing smoking quantity: a mediated mendelian randomization and observational mediation study.Respiratory research · 2026Observational
- Multi-cohort proteogenomic analyses reveal genetic effects across the proteome and diseasome.Cell · 2026Article
- A deep learning model to dynamically predict cancer-associated thromboembolism in large-scale healthcare systems.NPJ digital medicine · 2026Article
- Hepatic and abdominal adiposity in type 2 diabetes as assessed with machine learning on computed tomography scans.Diabetes, obesity & metabolism · 2026Article
- Article
- Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases.iScience · 2026Article
- Testing bidirectional associations of major depressive disorder with medical conditions: two-sample Mendelian randomization study.Npj mental health research · 2026Article
- Inclusion bias affects common variant discovery and replication in a health-system linked biobank.American journal of human genetics · 2026Article
- Genome-wide association meta-analyses over one million individuals identify 54 loci associated with urinary incontinence and its subtypes.medRxiv : the preprint server for health sciences · 2026Article
87 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
1 author.
Funding
Abstract
Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.
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.