SMART: a new patient similarity estimation framework for enhanced predictive modeling in acute kidney injury
SMART improves the identification of high-quality similar patient groups, enhancing the accuracy of personalized AKI prediction across various group sizes. By accurately identifying clinically relevant similar patients, clinicians can tailor treatments more effectively, advancing personalized care.
Authors
- Dana Y. Fuhrman (ORCID: https://orcid.org/0000-0002-3672-7960)
- Mei Liu (ORCID: https://orcid.org/0000-0002-8036-2110)
- Alan S.L. Yu (ORCID: https://orcid.org/0000-0002-1776-2533)
- Deyi Li
Institutions
- University of Pittsburgh (US)
- University of Florida Health (US)
- University of Florida (US)
- University of Kansas Medical Center (US)
- Children's Hospital of Pittsburgh (US)
- Florida College (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1093/jamia/ocaf125
- Citations
- 3
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases