文献类型：期刊文献

英文题名：Feature rearrangement based deep learning system for predicting heart failure mortality

作者：Wang, Zhe[1,2];Zhu, Yiwen[2];Li, Dongdong[2];Yin, Yichao[3];Zhang, Jing[2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Meilong Rd 130,Mail Box 408, 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：191

外文期刊名：COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

收录：;EI(收录号：20200708182523);WOS:【SCI-EXPANDED(收录号:WOS:000546180100006)】；

基金：This work is supported by National Major Scientific and Technological Special Project for "Significant New Drugs Development" under grant no. 2019ZX09201004, Natural Science Foundation of China under grant no. 61672227, "Shuguang Program" supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under grant no. 17SG31, Natural Sci-ence Foundations of China under grant no. 61806078, the Special Fund Project for Shanghai Informatization Development in Big Data under grant 201901043, and National Key R&D Program of China under grant no. 2018YFC0910500.

语种：英文

外文关键词：Heart failure; Deep learning; Feature rearrangement convolution

摘要：Background and objective: Heart Failure is a clinical syndrome commonly caused by any structural or functional impairment. Fast and accurate mortality prediction for Heart Failure is essential to improve the health care of patients and prevent them from death. However, due to the imbalance problem and poor feature representation in Heart Failure data, mortality prediction of Heart Failure is difficult with some simple models. To handle these problems, this study is focused on proposing a fast and accurate Heart Failure mortality prediction framework. Methods: This paper proposes a feature rearrangement based deep learning system for heart failure mortality prediction. The proposed framework improves the performance of predicting heart failure mortality by handling imbalance problem and achieving better feature representation. This paper also proposes a method named Feature rearrangement based convolutional layer, which demonstrates that the order of the input features is essential for the convolutional network. Results: The proposed system is experimentally evaluated on real-world Heart Failure data collected from the EHR system of Shanghai Shuguang Hospital, where 10,198 in-patients records are extracted between March 2009 and April 2016. Internal comparison results illustrate that the proposed framework achieves the best performance for Heart Failure mortality prediction. Extensive experimental results compared with other machine learning methods demonstrate that the proposed method has the highest average accuracy and area under the curve while predicting the three goals of in-hospital mortality, 30-day mortality, and 1-year mortality. Finally, top 12 essential clinical features are mined with their chi-square scores, which can help to assist clinicians in the treatment and research of heart failure. Conclusions: The proposed method successfully predict different target in three observation windows. Feature rearrangement based convolutional layer and Focal loss are employed into the proposed framework, which helps promote the prediction accuracy of Heart Failure death. The proposed method is fast and accurate for predicting heart failure mortality, especially for imbalance situation. This paper also provide a reasonable pipeline to model EHRs data and handle imbalance problem in medical data. (C) 2020 Elsevier B.V. All rights reserved.