Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.

Journal of the American Medical Informatics Association : JAMIA - 1 Feb 2026

Wiley Laura K, Rasmussen Luke V, Levinson Rebecca T, Malinowski Jennnifer, Manemann Sheila M, Wilson Melissa P, Chapman Martin, Pacheco Jennifer A, Walunas Theresa L, Starren Justin B, Bielinski Suzette J, Richesson Rachel L

Abstract excerpt

BACKGROUND: Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. OBJECTIVE: To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse....

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Electronic Health Records

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 41223026
DOI: 10.1093/jamia/ocaf195
PMCID: PMC12844593

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.PMID 41223026Read ↗

A general framework for developing computable clinical phenotype algorithms.Semantic proximity Read ↗

Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.Semantic proximity Read ↗

Trends and opportunities in computable clinical phenotyping: A scoping review.Semantic proximity Read ↗

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record DataSemantic proximity Read ↗

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical ReviewSemantic proximity Read ↗

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping With Electronic Health Record Data.Semantic proximity Read ↗

An online tool for correcting verification bias when validating electronic phenotyping algorithmsSemantic proximity Read ↗

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.Semantic proximity Read ↗

Machine learning approaches for electronic health records phenotyping: a methodical review.Semantic proximity Read ↗

Analyzing the heterogeneity of rule-based EHR phenotyping algorithms in CALIBER and the UK BiobankSemantic proximity Read ↗

Unsupervised Coverage Sampling to Enhance Clinical Chart Review Coverage for Computable Phenotype Development: Simulation and Empirical Study.Semantic proximity Read ↗

The Impact of "Possible Patients" on Phenotyping Algorithms: Electronic Phenotype Algorithms Can Only Be Reproduced by Sharing Detailed Annotation Criteria.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.