A Real-Time Dynamic Warning Method for MODS in Trauma Sepsis Patients Based on a Pre-Trained Transfer Learning Algorithm

TL;DR

This paper introduces a real-time warning system for MODS in trauma sepsis patients using a pre-trained AI model that improves early risk assessment and clinical decision-making.

Contribution

The novel contribution is a pre-trained transfer learning model with strong generalizability and interpretability for MODS prediction in trauma sepsis.

Findings

The pre-trained model achieved an average AUC of 0.906 across 6-, 12-, and 24-hour prediction windows.

Fine-tuning on 100 trauma sepsis cases yielded an AUC of 0.846, outperforming non-pre-trained models by 0.165.

SHAP analysis identified platelet count as a key variable in MODS prediction.

Abstract

Objectives: Multiple organ dysfunction syndrome (MODS) is a serious, prognostically poor complication in trauma sepsis. We developed an interpretable, multicenter-validated prediction model to enable early, individualized risk assessment and guide timely care. Methods: Using MIMIC-IV and eICU data, we built a pre-trained transfer-learning model with a separation processing strategy and assessed interpretability with SHAP. Results: Internal validation included 700 MIMIC-IV patients; external validation included 110 eICU patients. Across 6-, 12-, and 24-h prediction windows, the best pre-trained model achieved an average AUC of 0.906. Notably, fine-tuning on only 100 trauma sepsis cases (3.6% of the training set) still yielded an AUC of 0.846, surpassing the non-pre-trained model by 0.165. SHAP analysis further revealed that platelet count was one of the most important variables contributing to MODS prediction. Conclusions: Overall, the pre-trained MODS model demonstrated robust discrimination, generalizability, and clear interpretability in both internal and external validations, highlighting its portability and clinical potential for early identification of high-risk trauma sepsis patients.

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