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

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Abstract

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.

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Acknowledgments

We would like to thank all contributions made from doctors, nurses, and researchers from the Department of Emergency Medicine, Singapore General Hospital.

Author information

Authors and Affiliations

  1. Health Services Research Centre, Singapore Health Services, Singapore, Singapore

    Nan Liu

  2. Duke-NUS Medical School, National University of Singapore, Singapore, Singapore

    Nan Liu & Marcus Eng Hock Ong

  3. Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore

    Nan Liu, Zhi Xiong Koh & Marcus Eng Hock Ong

  4. School of Medicine, Duke University, Durham, NC, USA

    Jeffrey Tadashi Sakamoto

  5. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  6. SingHealth Emergency Medicine Residency Program, Singapore, Singapore

    Andrew Fu Wah Ho

  7. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Zhiping Lin

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  1. Nan Liu
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  7. Marcus Eng Hock Ong
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Corresponding author

Correspondence to Nan Liu.

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Funding

This study was supported by Singapore National Health Innovation Centre (NHIC) Innovation to Develop Grant (NHIC-I2D-1409014) and SingHealth Foundation Grant (SHF/FG543P/2013).

Conflict of Interests

Nan Liu and Marcus Eng Hock Ong have a patent filing that is related to this study (System and method of determining a risk score for triage, Application Number: US 13/791,764). Zhiping Lin and Marcus Eng Hock Ong have a similar patent filing related to this study (Method of predicting acute cardiopulmonary events and survivability of a patient, Application Number: US 13/047,348). Zhiping Lin and Marcus Eng Hock Ong also have a licensing agreement with ZOLL Medical Corporation for the above patented technology. There are no further patents, products in development or marketed products to declare. All the other authors do not have either commercial or personal associations or any sources of support that might pose a conflict of interest in the subject matter or materials discussed in this manuscript.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by SingHealth’s Centralized Institutional Review Board (CIRB, Ref: 2014/584/C) with waiver of patient consent.

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Informed consent was waived in the ethical approval.

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Liu, N., Sakamoto, J.T., Cao, J. et al. Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events. Cogn Comput 9, 545–554 (2017). https://doi.org/10.1007/s12559-017-9455-7

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  • DOI: https://doi.org/10.1007/s12559-017-9455-7

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