Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients. (20th August 2014) - Liu, Nan; Cao, Jiuwen; Koh, Zhi Xiong; Pek, Pin Pin; Ong, Marcus Eng Hock
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.