Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events

被引:0
作者
Nan Liu
Jeffrey Tadashi Sakamoto
Jiuwen Cao
Zhi Xiong Koh
Andrew Fu Wah Ho
Zhiping Lin
Marcus Eng Hock Ong
机构
[1] Health Services Research Centre,Duke
[2] Singapore Health Services,NUS Medical School
[3] National University of Singapore,School of Medicine
[4] Department of Emergency Medicine,Institute of Information and Control
[5] Singapore General Hospital,School of Electrical and Electronic Engineering
[6] Duke University,undefined
[7] Hangzhou Dianzi University,undefined
[8] SingHealth Emergency Medicine Residency Program,undefined
[9] Nanyang Technological University,undefined
来源
Cognitive Computation | 2017年 / 9卷
关键词
Extreme learning machine; Ensemble learning; Adverse cardiac events; Emergency department;
D O I
暂无
中图分类号
学科分类号
摘要
Accurate prediction of adverse cardiac events for the emergency department (ED) chest pain patients is essential in risk stratification due to the current ambiguity in diagnosing acute coronary syndrome. While most current practices rely on human decision by measuring clinical vital signs, computerized solutions are gaining popularity. We have previously proposed an ensemble-based scoring system (ESS). In this paper, we aim to extend the ESS system using extreme learning machine (ELM), a fast learning algorithm for neural networks. We recruited patients from the ED of Singapore General Hospital, and extracted features such as heart rate variability, 12-lead ECG parameters, and vital signs. We also proposed a novel algorithm called ESS-ELM to predict adverse cardiac events. Different from the original ESS algorithm, ESS-ELM uses the under-sampling technique only in model training. Our proposed method was compared to the original ESS algorithm and several clinical scores in predicting patient outcome. With a cohort of 797 recruited patients, we demonstrated that ESS-ELM outperformed the original ESS algorithm and three established clinical scores, namely HEART, TIMI, and GRACE, in terms of receiver operating characteristic analysis. Furthermore, we have investigated the impact of hidden node number and ensemble size on the predictive performance. ELM has demonstrated the flexibility in its integration with the ESS algorithm. Experiments showed the value of ESS-ELM in prediction of adverse cardiac events. Future works may include the use of new ELM-based learning methods and further validation with a new cohort of patients.
引用
收藏
页码:545 / 554
页数:9
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