Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients
Nan Liu, Jiuwen Cao, Zhi Xiong Koh, Pin Pin Pek, Marcus Eng Hock Ong · Mathematical Problems in Engineering · 2014
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.