Please use this identifier to cite or link to this item: https://doi.org/10.1155/2014/248938
Title: Risk stratification with extreme learning machine: A retrospective study on emergency department patients
Authors: Liu, N 
Cao, J
Koh, Z.X
Pek, P.P
Ong, M.E.H 
Keywords: Emergency departments
Extreme learning machine
Risk stratification
Issue Date: 2014
Publisher: Hindawi Publishing Corporation
Citation: Liu, N, Cao, J, Koh, Z.X, Pek, P.P, Ong, M.E.H (2014). Risk stratification with extreme learning machine: A retrospective study on emergency department patients. Mathematical Problems in Engineering 2014 : 248938. ScholarBank@NUS Repository. https://doi.org/10.1155/2014/248938
Rights: Attribution 4.0 International
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. © 2014 Nan Liu et al.
Source Title: Mathematical Problems in Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/180167
ISSN: 1024-123X
DOI: 10.1155/2014/248938
Rights: Attribution 4.0 International
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