Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records
Leveraging clinical terminologies incorporated by PheMAP can effectively identify patients with phenotypes who lack EHR diagnosis codes, thereby enhancing phenotyping quality and related research reliability.
Authors
- Chao Yan (ORCID: https://orcid.org/0000-0002-6719-1388)
- Rut Thakkar (ORCID: https://orcid.org/0000-0002-7993-7212)
- Peter J. Embí (ORCID: https://orcid.org/0000-0002-7733-0847)
- Alyson L. Dickson (ORCID: https://orcid.org/0000-0003-3404-3802)
- QiPing Feng (ORCID: https://orcid.org/0000-0002-6213-793X)
- Vern Eric Kerchberger (ORCID: https://orcid.org/0000-0002-0342-1965)
- Monika E. Grabowska (ORCID: https://orcid.org/0000-0003-0708-676X)
- Joshua C. Denny (ORCID: https://orcid.org/0000-0002-3049-7332)
- Bradley Malin (ORCID: https://orcid.org/0000-0003-3040-5175)
- Wei-Qi Wei
Institutions
- Vanderbilt University (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-03-29
- DOI
- https://doi.org/10.1093/jamia/ocaf055
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Institutes of Health