Construction and comprehensive explainability analysis of real-time sepsis prediction model based on machine learning
LI Jian
ZHANG Mingwei
ZHANG Tianyi
Abstract：Aiming at the problems of poor real-time and interpretability of sepsis prediction model based on machine learning,we designed a sepsis real-time prediction model with high timeliness and clinical explainability.Among them,the real-time prediction module could quickly obtain the 3 h dynamic feature sequence of non-invasive physiological indicators,and calculated the mean,standard deviation and end value.The interpretation module introduced Shapley additive interpretation method(TreeSHAP)based on tree structure,which could comprehensively improve the interpretability of the real-time prediction model of sepsis from the perspective of single prediction and global interpretation.The result showed that the accuracy,sensitivity and area under the curve of the sepsis real-time prediction model reached 0.71(95%CI,0.69～0.73),0.71(95%CI,0.70～0.73)and 0.76(95%CI,0.75～0.77),respec-tively.This model can not only provide real-time dynamic early warning for sepsis in critically ill patients,but also help clinicians deeply understand the generated details and the overall logic of the model,improve the clinical credibility of the model,and offer sup-port for clinical decision-making.
Keywords：SepsisReal-time predictionExplainability analysisMachine learningDynamic early warning
Publication Date：2025-06-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：7( 143-149 )