Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients. (20th August 2014)

• Record Type: Journal Article
• Authors: Liu, Nan; Cao, Jiuwen; Koh, Zhi Xiong; Pek, Pin Pin; Ong, Marcus Eng Hock
• Other Names: Sun Zhan-li Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-08-20
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/248938
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis.