ArticleJournal of the American Medical Informatics Association : JAMIA2023
Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Large language models facilitate the generation of electronic health record phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping Algorithms.medRxiv : the preprint server for health sciences · 2024Article
- Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
objectiveA previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP. MATERIALS AND
methodsWe compared how each resource produced top-ranked concept unique identifiers (CUIs) by term frequency-inverse document frequency with Jaccard matrices comparing single resources and the original PheMAP. We correlated top-ranked concepts from each resource to features used in established Phenotype KnowledgeBase (PheKB) algorithms for hypothyroidism, type II diabetes mellitus (T2DM), and dementias. Using resources separately, we calculated multiple phenotype risk scores for individuals from Vanderbilt University Medical Center's BioVU DNA Biobank and compared phenotyping performance against rule-based eMERGE algorithms. Lastly, we implemented an ensemble strategy which classified patient case/control status based upon PheMAP resource agreement.
resultsJaccard similarity matrices indicate that the similarity of CUIs comprising single resource-based PheMAPs varies. Single resource-based PheMAPs generated from MedlinePlus and MedicineNet outperformed others but only encompass 81.6% of overall disease phenotypes. We propose the PheMAP-Ensemble which provides higher average accuracy and precision than the combined average accuracy and precision of single resource-based PheMAPs. While offering complete phenotype coverage, PheMAP-Ensemble significantly increases phenotyping recall compared to the original iteration.
conclusionsResources comprising the PheMAP produce different phenotyping performance when implemented individually. The ensemble method significantly improves the quality of PheMAP by fully utilizing dissimilar resources to capture accurate phenotyping data from EHRs.
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