Use of Deep Learning for Continuous Prediction of Mortality for All Admissions in Intensive Care Units  

作　　者：Guangjian Zeng Jinhu Zhuang Haofan Huang Mu Tian Yi Gao Yong Liu Xiaxia Yu 

机构地区：[1]School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen 518060,China [2]Shenzhen Hospital,Southern Medical University,Shenzhen 518060,China

出　　处：《Tsinghua Science and Technology》2023年第4期639-648,共10页清华大学学报（自然科学版（英文版）

基　　金：This research was supported by the Key Discipline Fund of Shenzhen Hospital of Southern Medical University(No.2021-2023ICU);the New-Generation Information Technology by the Scientific Research Platform of Institutions of Higher Education of the Education Department of Guangdong Province(No.2021ZDZX1014);the Shenzhen University(SZU)Top Ranking Project(No.86000000210)。

摘　　要：The mortality rate in the intensive care unit(ICU)is a key metric of hospital clinical quality.To enhance hospital performance,many methods have been proposed for the stratification of patients’different risk categories,such as severity scoring systems and machine learning models.However,these methods make capturing time sequence information difficult,posing challenges to the continuous assessment of a patient’s severity during their hospital stay.Therefore,we built a predictive model that can make predictions throughout the patient’s stay and obtain the patient’s risk of death in real time.Our proposed model performed much better than other machine learning methods,including logistic regression,random forest,and XGBoost,in a full set of performance evaluation processes.Thus,the proposed model can support physicians’decisions by allowing them to pay more attention to high-risk patients and anticipate potential complications to reduce ICU mortality.

关 键 词：deep learning representation learning MORTALITY risk prediction critical care 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]