Early, unplanned readmission in diabetes inpatients: Comparing the utility and performance of machine learning and traditional prediction models to guide timely diabetes management team review
EUR prediction models facilitate targeted, timely DMT review. Regularised regression models offer a feasible, practical approach for identifying high-risk patients in resource-limited hospital settings. Combining model-identified risk with abnormal glucose refines patient selection, optimising resource allocation.
Authors
- Barbara Depczynski (ORCID: https://orcid.org/0000-0002-9901-1652)
- Sue Mei Lau (ORCID: https://orcid.org/0000-0002-7351-3064)
- Gordana Popović (ORCID: https://orcid.org/0000-0002-1376-1058)
- Ben Maslen (ORCID: https://orcid.org/0000-0003-3475-6247)
Institutions
- New South Wales Department of Primary Industries (AU)
- UNSW Sydney (AU)
- Prince of Wales Hospital (AU)
Publication Details
- Journal
- Diabetic Medicine
- Published
- 2025-12-06
- DOI
- https://doi.org/10.1111/dme.70186
- Citations
- 1
- Primary Topic
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Type
- article
- Field-Weighted Citation Impact
- 1.29