文献类型：期刊文献

英文题名：Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction

作者：Wang, Zhe[1,2];Wang, Bolu[2];Zhou, Yangming[2];Li, Dongdong[2];Yin, Yichao[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, 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：2020

卷号：101

外文期刊名：JOURNAL OF BIOMEDICAL INFORMATICS

收录：;EI(收录号：20220511568448);WOS:【SCI-EXPANDED(收录号:WOS:000525735000012)】；

基金：This work is sponsored by "Shuguang Program" supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, Natural Science Foundation of China under Grant No. 61672227, 61903144, and 61806078, Shanghai Sailing Program under Grant 19YF1412400, National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant No. 2019ZX09201004, and the Special Fund Project for Shanghai Informatization Development in Big Data under Grant 201901043.

语种：英文

外文关键词：Heart Failure; Mortality Prediction; Electronic Health Records; Feature Selection; Multiple Kernel Learning

摘要：Heart Failure (HF) is one of the most common causes of hospitalization and is burdened by short-term (inhospital) and long-term (6-12 month) mortality. Accurate prediction of HF mortality plays a critical role in evaluating early treatment effects. However, due to the lack of a simple and effective prediction model, mortality prediction of HF is difficult, resulting in a low rate of control. To handle this issue, we propose a Weight-based Multiple Empirical Kernel Learning with Neighbor Discriminant Constraint (WMEKL-NDC) method for HF mortality prediction. In our method, feature selection by calculating the F-value of each feature is first performed to identify the crucial clinical features. Then, different weights are assigned to each empirical kernel space according to the centered kernel alignment criterion. To make use of the discriminant information of samples, neighbor discriminant constraint is finally integrated into multiple empirical kernel learning framework. Extensive experiments were performed on a real clinical dataset containing 10, 198 in-patients records collected from Shanghai Shuguang Hospital in March 2009 and April 2016. Experimental results demonstrate that our proposed WMEKL-NDC method achieves a highly competitive performance for HF mortality prediction of inhospital, 30-day and 1-year. Compared with the state-of-the-art multiple kernel learning and baseline algorithms, our proposed WMEKL-NDC is more accurate on mortality prediction Moreover, top 10 crucial clinical features are identified together with their meanings, which are very useful to assist clinicians in the treatment of HF disease.