ArticleJournal of the American Medical Informatics Association : JAMIA2018
Enabling phenotypic big data with PheNorm.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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Who cites it
65 citing papers in PubMed.
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Phenotyping antidepressant treatment response with deep learning in electronic health records.Translational psychiatry · 2026Article
- Identifying anaphylaxis using weakly-supervised prediction models and natural language processing.medRxiv : the preprint server for health sciences · 2026Article
- Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing.Scientific reports · 2026Article
- Estimate renal cell carcinoma recurrence rates using electronic health records.ESMO real world data and digital oncology · 2026Article
- Aortic and Cardiac Structure From Routine CT Predict Cardiovascular Risk Beyond PREVENT and Coronary Calcium.JACC. Cardiovascular imaging · 2026Article
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- Integrating large scale genetic and clinical information to predict cases of heart failure.Communications medicine · 2025Article
- Cross-Site Predictions of Readmission After Psychiatric Hospitalization With Mood or Psychotic Disorders: Retrospective Study.JMIR mental health · 2025Article
- External validation and comparison of the AHA-PREVENT and pooled cohort equations for 10-year ASCVD risk prediction in a contemporary US EHR population.American heart journal plus : cardiology research and practice · 2025Article
- An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 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
- ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.Journal of biomedical informatics · 2025Article
- Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort.International journal of cardiology. Heart & vasculature · 2025Article
- Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A hybrid framework with large language models for rare disease phenotyping.BMC medical informatics and decision making · 2024Article
- A general framework for developing computable clinical phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Defining a Minimal Benchmark for Cardiovascular Risk Prediction Calculators in New England Electronic Health Record-Derived Cohort.Circulation. Cardiovascular quality and outcomes · 2024Article
- Article
- Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.Journal of the American Medical Informatics Association : JAMIA · 2024Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Objective: Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. Methods: The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. Results: We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. Conclusion: The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data.
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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.