ArticleJournal of the American Medical Informatics Association : JAMIA2020
PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- A Systematic Review to Summarize and Critically Appraise Existing Phenotype Libraries Using Electronic Health Records.Pharmacoepidemiology and drug safety · 2026Pooled it
- Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.Kidney international · 2022Guideline
- Embeddings of clinical codes enable knowledge-grounded AI in medicine.NPJ digital medicine · 2026Article
- Population differences in wearable device wear time: Rescuing data to address biases and advance health equity.medRxiv : the preprint server for health sciences · 2026Article
- Patterns and Clinical Outcomes of Physical Activity and Sedentary Behavior Across 20 Million Days of Wearable Monitoring in U.S. Adults.medRxiv : the preprint server for health sciences · 2026Article
- Discovery of a Polymorphic Gene Fusion via Bottom-Up Chimeric RNA Prediction: Case Study.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All of Us Research Program: Retrospective Observational Longitudinal Study.Journal of medical Internet research · 2025Observational
- Diversity and longitudinal records: Genetic architecture of disease associations and polygenic risk in the Taiwanese Han population.Science advances · 2025Article
- Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Multiomics reveal key inflammatory drivers of severe obesity: IL4R, LILRA5, and OSM.Cell genomics · 2025Article
- Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study.JAMIA open · 2025Review
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.Nature communications · 2025Article
- Large language models facilitate the generation of electronic health record phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program.Nature medicine · 2024Article
- Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.Journal of biomedical informatics · 2024Article
- Distinguishing vulnerability and resilience to posttraumatic stress disorder evaluating traumatic experiences, genetic risk and electronic health records.Psychiatry research · 2024Article
- A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology.Mayo Clinic proceedings. Digital health · 2024Article
- Discovery of a polymorphic gene fusion via bottom-up chimeric RNA prediction.Nucleic acids research · 2024Article
- Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveDeveloping algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs. MATERIALS AND
methodsPheMap is a knowledge base of medical concepts with quantified relationships to phenotypes that have been extracted by natural language processing from publicly available resources. PheMap searches EHRs for each phenotype's quantified concepts and uses them to calculate an individual's probability of having this phenotype. We compared PheMap to clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network for type 2 diabetes mellitus (T2DM), dementia, and hypothyroidism using 84 821 individuals from Vanderbilt Univeresity Medical Center's BioVU DNA Biobank. We implemented PheMap-based phenotypes for genome-wide association studies (GWAS) for T2DM, dementia, and hypothyroidism, and phenome-wide association studies (PheWAS) for variants in FTO, HLA-DRB1, and TCF7L2.
resultsIn this initial iteration, the PheMap knowledge base contains quantified concepts for 841 disease phenotypes. For T2DM, dementia, and hypothyroidism, the accuracy of the PheMap phenotypes were >97% using a 50% threshold and eMERGE case-control status as a reference standard. In the GWAS analyses, PheMap-derived phenotype probabilities replicated 43 of 51 previously reported disease-associated variants for the 3 phenotypes. For 9 of the 11 top associations, PheMap provided an equivalent or more significant P value than eMERGE-based phenotypes. The PheMap-based PheWAS showed comparable or better performance to a traditional phecode-based PheWAS. PheMap is publicly available online.
conclusionsPheMap significantly streamlines the process of extracting research-quality phenotype information from EHRs, with comparable or better performance to current phenotyping approaches.
Indexed as
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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.