ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Phenotyping in distributed data networks: selecting the right codes for the right patients.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Real-World Use of Controlled Terminologies, Ontologies, and Vocabularies for Evidence Generation Across a Large International Observational Network: Challenges and Lessons Learned From a Mixed Method Study.JMIR medical informatics · 2026Article
- The burden of hand trauma surgery on primary care in the United Kingdom: a nation-wide analysis of antibiotic and opioid prescriptions.The Journal of hand surgery, European volume · 2025Article
- Methods for identifying health status from routinely collected health data: An overview.Integrative medicine research · 2025Article
- CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization.PloS one · 2025Article
- Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.Ophthalmology. Retina · 2024Observational
- Article
- 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
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 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
3 authors.
Funding
Abstract
Observational data can be used to conduct drug surveillance and effectiveness studies, investigate treatment pathways, and predict patient outcomes. Such studies require developing executable algorithms to find patients of interest or phenotype algorithms. Creating reliable and comprehensive phenotype algorithms in data networks is especially hard as differences in patient representation and data heterogeneity must be considered. In this paper, we discuss a process for creating a comprehensive concept set and a recommender system we built to facilitate it. PHenotype Observed Entity Baseline Endorsements (PHOEBE) uses the data on code utilization across 22 electronic health record and claims datasets mapped to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model from the 6 countries to recommend semantically and lexically similar codes. Coupled with Cohort Diagnostics, it is now used in major network OHDSI studies. When used to create patient cohorts, PHOEBE identifies more patients and captures them earlier in the course of the disease.
Indexed as
Identifiers
37128407PMC10148372What 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.