JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (1): 26-35.doi: 10.6040/j.issn.1671-9352.8.2024.009

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Analysis of the prediction model based on deep neural networks for mortality risk prediction for sepsis patients in intensive care units

YU Lei1, SUN Yi2, HUA Jinming2, LI Laquan3   

  1. 1. Emergency Department, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China;
    2. International College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
    3. School of Science, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Published:2026-01-15

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Abstract: A deep neural network(DNN)model is proposed by integrating variable selection networks(VSN)and gated residual networks(GRN)to predict the 30-day mortality risk of sepsis patients in the intensive care unit(ICU)and to conduct an in-depth interpretability analysis. In the critical care medical database, 43 significant features are selected using a random forest algorithm, and the proposed model is employed to evaluate mortality risk. The remove and retrain(ROAR)method is utilized to determine the optimal interpretability approach for explaining the results. Testing outcomes indicate that the proposed model outperforms other machine learning models, achieving an area under the receiver operating characteristic curve(AUROC)of 0.967. In the ROAR analysis, the AUROC of the layer-wise relevance propagation(LRP)method decreases from 0.967 to 0.828. Through interpretability analysis of the proposed model using LRP, the Charlson comorbidity score is identified as the most critical feature. In contrast, the organ failure score, age, and respiratory rate also have a pronounced impact on the mortality risk of ICU sepsis patients.

Key words: sepsis, mortality risk prediction, deep neural network, remove and retrain, layer-wise relevance propagation

CLC Number: 

  • TP391
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