# A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction Source: [A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:17:45.746Z Authors: Meryem Altin Karagoz, Marc D. Breton, Anas El Fathi Published: 2025-05-12T20:22:44.000Z ## Abstract Accurate blood glucose prediction can enable novel interventions for type 1 diabetes treatment, including personalized insulin and dietary adjustments. Although recent advances in transformer-based architectures have demonstrated the power of attention mechanisms in complex multivariate time series prediction, their potential for blood glucose (BG) prediction remains underexplored. We present a comparative analysis of transformer models for multi-horizon BG prediction, examining forecasts up to 4 hours and input history up to 1 week. The publicly available DCLP3 dataset (n=112) was split (80%-10%-10%) for training, validation, and testing, and the OhioT1DM dataset (n=12) served as an external test set. We trained networks with point-wise, patch-wise, series-wise, and hybrid embeddings, using CGM, insulin, and meal data. For short-term blood glucose prediction, Crossformer, a patch-wise transformer architecture, achieved a superior 30-minute prediction of RMSE (15.6 mg / dL on OhioT1DM). For longer-term predictions (1h, 2h, and 4h), PatchTST, another path-wise transformer, prevailed with the lowest RMSE (24.6 mg/dL, 36.1 mg/dL, and 46.5 mg/dL on OhioT1DM). In general, models that used tokenization through patches demonstrated improved accuracy with larger input sizes, with the best results obtained with a one-week history. These findings highlight the promise of transformer-based architectures for BG prediction by capturing and leveraging seasonal patterns in multivariate time-series data to improve accuracy. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Blood Glucose Forecasting on OhioT1DM Combined (test)](): Blood Glucose Forecasting; OhioT1DM Combined (test); RMSE: 27.71. - [Blood Glucose Forecasting on OhioT1DM 2020 (test)](): Blood Glucose Forecasting; OhioT1DM 2020 (test); RMSE: 31.19. ## Original sources and implementation [Original paper]() [PDF]()