Risk Stratification with Extreme Learning Machine : A Retrospective Study on Emergency Department Patients

Abstract EN

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.