Document Type
Article
Abstract
Sepsis-induced glucose fluctuations present major challenges in critical care, underscoring the importance of accurate glucose monitoring and forecasting to improve patient outcomes. This study introduces a suite of forecasting models trained using continuous glucose monitoring data from a diabetic patient with sepsis (19,621 data points). The models include four transformer-based ones (iTransformer, Crossformer, PatchTST, FEDformer), a dynamic linear model (DLinear), and an ensemble zero-shot inference method leveraging ChatGPT-4. Model performance was evaluated for 15-, 30-, and 60-minute prediction horizons with an optimized 30-minute lookback window. PatchTST achieved the lowest mean maximum percentage error (MMPE) for short-term forecasts (3.0% at 15 minutes), while DLinear excelled at longer horizons (7.46% and 14.41% MMPE at 30 and 60 minutes, respectively). The ensemble ChatGPT-4 approach also showed competitive results. Overall, this work offers a toolbox of advanced forecasting models for ICU glucose prediction and management. The comprehensive comparison among the models highlights the promise of machine learning models–particularly DLinear and PatchTST–in supporting glucose monitoring and ultimately digital twin implementations, paving the way toward personalized and adaptive glycemic control in septic patients.
Digital Object Identifier (DOI)
Publication Info
Published in Scientific Reports, Volume 15, 2025, pages 40432-.
APA Citation
He, J.-X., Jiang, H., & Wang, Q. (2025). Machine learning glucose forecasting models for septic patients. Scientific Reports, 15, 40432.https://doi.org/10.1038/s41598-025-23057-0
Rights
© The Author(s) 2025 This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.