数据采集与处理2026，Vol.41Issue(3)：869-881,13.DOI:10.16337/j.1004-9037.2026.03.018
基于多特征联合学习的脓毒症死亡风险预测模型
A Sepsis Mortality Risk Prediction Model Based on Multi-feature Federated Learning
摘要
Abstract
Sepsis refers to a systemic inflammatory response resulting from infections,and it carries a high risk of mortality in intensive care settings.Existing predictive models often rely on extracting single feature subsets from a larger set,failing to fully utilize the complex interactions between feature subsets,known as structural mutual information.This limitation reduces prediction accuracy.Structural mutual information not only captures dependencies between features at the same level of granularity but also reveals complex relationships across different granularities,enabling more precise detection of subtle changes in a patient's condition.To address this limitation,this study presents a novel sepsis prognosis model that deeply explores the structural mutual information within electronic health records,significantly enhancing the accuracy of mortality risk predictions.Experimental results show that the proposed model achieves notable improvements in predictive accuracy,providing clinicians with more dependable mortality risk assessments and clearer decision-making support.关键词
深度学习/集成学习/特征学习/脓毒症预后/决策支持Key words
deep learning/integrated learning/feature learning/sepsis prognosis/decision support分类
信息技术与安全科学引用本文复制引用
文婷,余雷,李腊全..基于多特征联合学习的脓毒症死亡风险预测模型[J].数据采集与处理,2026,41(3):869-881,13.基金项目
国家自然科学基金(61902046,61901074,62076044) （61902046,61901074,62076044）
中国博士后科学基金(2021M693771) （2021M693771）
重庆市自然科学基金(CSTB2022NSCQ-MSX0145). National Natural Science Foundation of China(Nos.61902046,61901074,62076044) （CSTB2022NSCQ-MSX0145）
China Postdoctoral Science Foundation(No.2021M693771) （No.2021M693771）
Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-MSX0145). （No.CSTB2022NSCQ-MSX0145）
