文献类型：期刊文献

英文题名：Mortality prediction system for heart failure with orthogonal relief and dynamic radius means

作者：Wang, Zhe[1,2];Yao, Lijuan[2];Li, Dongdong[2];Ruan, Tong[2];Liu, Min[3];Gao, Ju[3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]Shanghai Shuguang Hosp, Shanghai 200021, Peoples R China

年份：2018

卷号：115

起止页码：10

外文期刊名：INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

收录：;EI(收录号：20181705046471);WOS:【SSCI(收录号:WOS:000432659000002)，SCI-EXPANDED(收录号:WOS:000432659000002)】；

基金：This work is supported by Natural Science Foundations of China under Grant No. 61672227, "Shuguang Program" supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, and "Action Plan for Innovation on Science and Technology" Projects of Shanghai under Grant No. 16511101000.

语种：英文

外文关键词：Mortality prediction; Heart failure; Feature selection; Dynamic radius; Machine learning

摘要：Objective: This paper constructs a mortality prediction system based on a real-world dataset. This mortality prediction system aims to predict mortality in heart failure (HF) patients. Effective mortality prediction can improve resources allocation and clinical outcomes, avoiding inappropriate overtreatment of low-mortality patients and discharging of high-mortality patients. This system covers three mortality prediction targets: prediction of in-hospital mortality, prediction of 30-day mortality and prediction of 1-year mortality. Materials and methods: HF data are collected from the Shanghai Shuguang hospital. 10,203 in-patients records are extracted from encounters occurring between March 2009 and April 2016. The records involve 4682 patients, including 539 death cases. A feature selection method called Orthogonal Relief (OR) algorithm is first used to reduce the dimensionality. Then, a classification algorithm named Dynamic Radius Means (DRM) is proposed to predict the mortality in HF patients. Results and discussions: The comparative experimental results demonstrate that mortality prediction system achieves high performance in all targets by DRM. It is noteworthy that the performance of in-hospital mortality prediction achieves 87.3% in AUC (35.07% improvement). Moreover, the AUC of 30-day and 1-year mortality prediction reach to 88.45% and 84.84%, respectively. Especially, the system could keep itself effective and not deteriorate when the dimension of samples is sharply reduced. Conclusions: The proposed system with its own method DRM can predict mortality in HF patients and achieve high performance in all three mortality targets. Furthermore, effective feature selection strategy can boost the system. This system shows its importance in real-world applications, assisting clinicians in HF treatment by providing crucial decision information.