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Hindawi, Mathematical Problems in Engineering, (2014), p. 1-6, 2014

DOI: 10.1155/2014/248938

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

Journal article published in 2014 by Nan Liu ORCID, Jiuwen Cao ORCID, Zhi Xiong Koh, Pin Pek ORCID, Marcus Eng Hock Ong
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.