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Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy.

Jinmei Wu1, Xianwei Zhang2, Chenglong Liang1,3,4

  • 1The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.

BMC Medical Informatics and Decision Making
|February 4, 2026
PubMed
Summary

A machine learning model accurately predicts 28-day mortality in sepsis-induced coagulopathy (SIC) patients. This XGBoost model aids clinical decisions for better patient outcomes.

Keywords:
Machine learningPrognosisSHAPSepsisSepsis-induced coagulopathy

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Area of Science:

  • Critical Care Medicine
  • Medical Informatics
  • Computational Biology

Background:

  • Sepsis-induced coagulopathy (SIC) is a frequent complication of sepsis.
  • SIC is associated with increased mortality risk in sepsis patients.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting 28-day mortality in patients with SIC.
  • To identify key predictors of mortality in SIC patients.

Main Methods:

  • Data from the MIMIC-IV database was used for model training and external validation.
  • Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression identified predictive factors.
  • XGBoost classification model was developed and evaluated using ROC curves, calibration curves, and DCA.
  • SHAP values were used for model interpretability.

Main Results:

  • The XGBoost model achieved an AUC of 0.840 on the test set, with 80.7% accuracy.
  • External validation showed excellent performance with an AUC of 0.864.
  • The model demonstrated strong predictive power for 28-day mortality in SIC patients.

Conclusions:

  • An interpretable XGBoost model was successfully developed to predict 28-day mortality in SIC patients.
  • The model can support clinical decision-making and personalized treatment strategies.
  • This tool offers a valuable basis for assessing mortality risk in SIC.