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Updated: Jan 11, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Machine learning glucose forecasting models for septic patients.

Xiang Cao¹, Dong Wang^2,3, Jue-Xia He^2,3,4

¹Department of Mathematics, University of South Carolina, Columbia, SC, 29208, USA.

Scientific Reports

|November 18, 2025

Summary

Accurate glucose forecasting in sepsis is crucial for critical care. This study shows advanced machine learning models like PatchTST and DLinear effectively predict glucose levels, aiding personalized patient management.

Keywords:

Forecasting Glucose Hypo/hyperglycemia Machine learning models Sepsis

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Last Updated: Jan 11, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Critical Care Medicine
Biomedical Engineering
Data Science

Background:

Sepsis-induced glucose fluctuations pose significant challenges in intensive care units (ICUs).
Accurate glucose monitoring and forecasting are vital for improving patient outcomes in sepsis.
Diabetic patients with sepsis experience complex glycemic variability.

Purpose of the Study:

To evaluate the performance of various advanced forecasting models for predicting glucose levels in a diabetic patient with sepsis.
To compare transformer-based models, a dynamic linear model, and an ensemble ChatGPT-4 method for glucose forecasting.
To identify optimal models for short-term and long-term glucose prediction in critical care settings.

Main Methods:

Trained multiple forecasting models including iTransformer, Crossformer, PatchTST, FEDformer, and DLinear on 19,621 continuous glucose monitoring data points.
Utilized an ensemble zero-shot inference method with ChatGPT-4.
Evaluated model performance using Mean Maximum Percentage Error (MMPE) across 15-, 30-, and 60-minute prediction horizons with a 30-minute lookback window.

Main Results:

PatchTST demonstrated the lowest MMPE (3.0%) for 15-minute glucose forecasts.
DLinear achieved superior performance for longer horizons, with MMPEs of 7.46% (30 minutes) and 14.41% (60 minutes).
The ensemble ChatGPT-4 approach also yielded competitive forecasting results.

Conclusions:

Advanced machine learning models, particularly DLinear and PatchTST, show significant promise for ICU glucose prediction and management.
These models can support continuous glucose monitoring and contribute to personalized glycemic control strategies for septic patients.
The findings pave the way for digital twin implementations in critical care for adaptive glucose management.