摘要
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.关键词
脓毒症/实时预测/可解释性分析/机器学习/动态预警Key words
Sepsis/Real-time prediction/Explainability analysis/Machine learning/Dynamic early warning分类
医药卫生
