GLM-DM: language model boosted neural networks for HbA1c trend prediction in diabetes mellitus
Proposed approach learns the underlying complex function of HbA1c using clinical variables easily available at the patient visit and leveraging the power of LMs to accurately predict the trend of HbA1c in a period. The model can enhance patient advisories for daily diabetes management without the need for continuous glucose monitoring.
Authors
- Patricia M Verona (ORCID: https://orcid.org/0000-0003-2641-1986)
- Xinrui He (ORCID: https://orcid.org/0009-0003-4475-8059)
- Yikun Ban (ORCID: https://orcid.org/0000-0003-3035-4849)
- Curtiss Bela Cook (ORCID: https://orcid.org/0000-0001-5885-9959)
- Jingrui He (ORCID: https://orcid.org/0000-0002-6429-6272)
Institutions
- University of Illinois Urbana-Champaign (US)
- Knowledge Integration (United Kingdom) (GB)
- Mayo Clinic in Arizona (US)
- Data Management Services (United States) (US)
Publication Details
- Journal
- Future Science OA
- Published
- 2025-11-11
- DOI
- https://doi.org/10.1080/20565623.2025.2567166
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00