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

Nan Liu | Zendy; Jiuwen Cao | Zendy; Zhi Xiong Koh | Zendy; Pin Pin Pek | Zendy; Marcus Eng Hock Ong | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Nan Liu,

Jiuwen Cao,

Zhi Xiong Koh,

Pin Pin Pek,

Marcus Eng Hock Ong

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/248938

Subject(s) - extreme learning machine , emergency department , risk stratification , support vector machine , machine learning , artificial intelligence , computer science , stratification (seeds) , emergency medicine , medicine , artificial neural network , biology , dormancy , seed dormancy , botany , germination , psychiatry

This paper presents a novel risk stratification method usingextreme learning machine (ELM). ELM was integrated into a scoringsystem to identify the risk of cardiac arrest in emergency department(ED) patients. The experiments were conducted on a cohort of 1025critically ill patients presented to the ED of a tertiary hospital. ELM andvoting based ELM (V-ELM) were evaluated. To enhance the predictionperformance, we proposed a selective V-ELM (SV-ELM) algorithm. Theresults showed that ELM based scoring methods outperformed supportvector machine (SVM) based scoring method in the receiver operationcharacteristic analysis

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research