ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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
43 citing papers in PubMed.
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 2026Article
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models.Proceedings of machine learning research · 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
- REM Sleep Behavior Disorder Diagnostic Code Accuracy and Implications in the Real-World Setting.Neurology. Clinical practice · 2025Article
- Enhancing Gen3 for clinical trial time series analytics and data discovery: a data commons framework for NIH clinical trials.Frontiers in digital health · 2025Article
- Endometriosis and autoimmunity: a large-scale case-control study of endometriosis and 10 distinct autoimmune diseases.npj women's health · 2025Article
- Towards automated phenotype definition extraction using large language models.Genomics & informatics · 2024Article
- Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- The Stanford Medicine data science ecosystem for clinical and translational research.JAMIA open · 2023Article
- Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.Journal of medical Internet research · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The widespread usage of electronic health records (EHRs) for clinical research has produced multiple electronic phenotyping approaches. Methods for electronic phenotyping range from those needing extensive specialized medical expert supervision to those based on semi-supervised learning techniques. We present Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE), an R- package phenotyping framework that combines noisy labeling and anchor learning. APHRODITE makes these cutting-edge phenotyping approaches available for use with the Observational Health Data Sciences and Informatics (OHDSI) data model for standardized and scalable deployment. APHRODITE uses EHR data available in the OHDSI Common Data Model to build classification models for electronic phenotyping. We demonstrate the utility of APHRODITE by comparing its performance versus traditional rule-based phenotyping approaches. Finally, the resulting phenotype models and model construction workflows built with APHRODITE can be shared between multiple OHDSI sites. Such sharing allows their application on large and diverse patient populations.
Identifiers
28815104PMC5543379What 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.