PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse
The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.
Authors
- Rebecca T. Levinson (ORCID: https://orcid.org/0000-0002-2775-7543)
- Suzette J. Bielinski (ORCID: https://orcid.org/0000-0002-2905-5430)
- Jennifer A. Pacheco (ORCID: https://orcid.org/0000-0001-8021-5818)
- Laura K. Wiley (ORCID: https://orcid.org/0000-0001-6681-9754)
- Melissa P. Wilson (ORCID: https://orcid.org/0000-0001-9176-3131)
- Jennifer Malinowski (ORCID: https://orcid.org/0000-0001-7500-2199)
- Sheila M. Manemann
- Justin Starren (ORCID: https://orcid.org/0000-0002-5403-1115)
- Luke V. Rasmussen (ORCID: https://orcid.org/0000-0002-4497-8049)
- Martin Chapman (ORCID: https://orcid.org/0000-0002-5242-9701)
- Theresa L. Walunas (ORCID: https://orcid.org/0000-0002-7653-3650)
- Rachel Richesson (ORCID: https://orcid.org/0000-0003-0279-7036)
Institutions
- Northwestern University (US)
- University of Arizona (US)
- King's College London (GB)
- Washington University in St. Louis (US)
- Heidelberg University (DE)
- University of Michigan (US)
- University Hospital Heidelberg (DE)
- Mayo Clinic in Arizona (US)
- Strategic Insight (United States) (US)
- New York Life Insurance Company (United States) (US)
- Mayo Clinic in Florida (US)
- University of Colorado Anschutz Medical Campus (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1093/jamia/ocaf195
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- U.S. National Library of Medicine
- National Center for Advancing Translational Sciences