arXiv:2607. 19006v1 Announce Type: new Abstract: Accurate forecasting of blood glucose concentration is key in the management of Type 1 Diabetes, facilitating early detection of adverse glycemic events and supporting timely therapeutic interventions.
By Giorgia Rigamonti, Mirko Paolo Barbato, Davide Marelli, Paolo Napoletano
arXiv:2601. 05353v2 Announce Type: replace Abstract: Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support the early prediction of dysglycemic risk.
By Shovito Barua Soumma, Hassan Ghasemzadeh
arXiv:2606. 05373v1 Announce Type: new Abstract: Reliable neural architecture selection is an open challenge in time-series forecasting under limited, noisy, and heterogeneous data, where standard heuristic architecture design and validation approaches fail to ensure accurate and reliable prediction and generalization.
By Md Azharul Islam, Dwyer Deighan, Tarunraj Singha, Danial Faghihi
arXiv:2607. 21117v1 Announce Type: cross Abstract: Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes management, particularly for type 1 diabetes.
By Davide Marelli, Giorgia Rigamonti, Mirko Paolo Barbato, Paolo Napoletano
arXiv:2606. 18640v1 Announce Type: new Abstract: Glucose forecasting algorithms are an important aspect of glycemic control management in type 1 diabetes.
By Nathaniel Jeffries, Miriam Wolff, Sam Royston, Elizabeth Healey, Caleb Mayer, David Klonoff, Michael Snyder, Tao Wang
GlucoFM is a lightweight foundation model for continuous glucose monitoring that aligns irregular CGM data to a 24‑hour grid and splits glucose dynamics into slow‑varying trend and short‑term deviation streams. Pre‑trained on over 109,000 hours of unlabeled recordings, it outperforms existing CGM‑specific models on seven phenotype‑classification tasks, improving average PR‑AUC by 4.1 points and enabling strong cross‑dataset transfer and few‑shot adaptation. When combined with meal, nutrition, and subject context, its frozen encoder delivers the lowest two‑hour postprandial glycemic response errors for trajectory, incremental AUC, peak rise, and peak timing metrics.
By Zechen Li, Keerthana Natarajan, Weizhi Zhang, Menglian Zhou, Simon A. Lee, Yuwei Zhang, Maxwell A. Xu, Zeinab Esmaeilpour, Flora D. Salim, Mark Malhotra, Lindsey Sunden, Shwetak Patel, Yuzhe Yang, Ahmed A. Metwally