<scp>GlucoNet</scp> ‐ <scp>MM</scp> : A multimodal attention‐based multi‐task learning framework with decision transformer for personalised and explainable blood glucose forecasting
GlucoNet-MM represents a promising step toward intelligent, personalized clinical decision support for diabetes care. Its multimodal design, policy-aware forecasting, and interpretability features enhance both prediction accuracy and clinical trust, enabling proactive glycemic management tailored to individual patient needs.
Authors
- Rytis Maskeliūnas (ORCID: https://orcid.org/0000-0002-2809-2213)
- Sarmad Maqsood (ORCID: https://orcid.org/0000-0002-1775-2589)
- Eglė Belousovienė (ORCID: https://orcid.org/0000-0003-2204-3442)
- Muhammad Abdullah Sarwar
Institutions
- Lithuanian University of Health Sciences (LT)
- Kaunas University of Technology (LT)
- Vytautas Magnus University (LT)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1111/dom.70147
- Citations
- 1
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 1.29