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<p>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.</p>

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Machine learning glucose forecasting models for septic patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Xiang Cao,
  • Dong Wang,
  • Jue-Xia He,
  • Jianguo Hou,
  • Jun Li,
  • Hua Jiang,
  • Qi Wang

摘要

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.