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.

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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
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article

GLM-DM: language model boosted neural networks for HbA1c trend prediction in diabetes mellitus

Patricia M Verona, Xinrui He, Yikun Ban, Curtiss Bela Cook et al.
Future Science OA
Artificial Intelligence in Healthcare
article

GLM-DM: language model boosted neural networks for HbA1c trend prediction in diabetes mellitus

Patricia M Verona, Xinrui He, Yikun Ban, Curtiss Bela Cook, Jingrui He
article en

Abstract

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.

Future Science OAVol. 11(1)
University of Illinois Urbana-Champaign (US), Knowledge Integration (United Kingdom) (GB), Mayo Clinic in Arizona (US), Data Management Services (United States) (US)
Openalex Percentile: Top 61%
Artificial Intelligence in Healthcare
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GLM-DM: language model boosted neural networks for HbA1c trend prediction in diabetes mellitus — Patricia M Verona, Xinrui He, et al. · Future Science OA (2025) | TGRS Research Map | TGRS