Comparing computable type 2 diabetes phenotype definitions in identifying populations of interest for clinical research
Our study highlights the complexity of computable T2D phenotypes in translating commonly agreed T2D clinical definitions when applied against retrospective EHR data. Our findings provide an understanding of using appropriate phenotypes to identify, enrol and analyse T2D populations of interest using EHR data.
Authors
- Star Liu (ORCID: https://orcid.org/0000-0002-7587-7451)
- Rita Rastogi Kalyani (ORCID: https://orcid.org/0000-0002-1530-6884)
- Hadi H.K. Kharrazi (ORCID: https://orcid.org/0000-0003-1481-4323)
- Priyanka Dua Sood (ORCID: https://orcid.org/0009-0005-6916-0381)
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
Publication Details
- Journal
- BMJ Health & Care Informatics
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1136/bmjhci-2024-101378
- Citations
- 1
- Primary Topic
- Diabetes Management and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.29
Funders
- Johns Hopkins University