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Int. J. Environ. Res. Public Health 2016, 13(9), 912; doi:10.3390/ijerph13090912

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

1
College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
2
Philips Research China—Healthcare, Shanghai 200233, China
3
Department of Cardiology, Chinese PLA General Hospital, Beijing 100853, China
*
Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Received: 22 April 2016 / Revised: 9 August 2016 / Accepted: 31 August 2016 / Published: 13 September 2016
(This article belongs to the Special Issue Health Informatics and Public Health)
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Abstract

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. Results: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. Conclusions: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations. View Full-Text
Keywords: MACE prediction; acute coronary syndrome; admission record; hybrid model; risk factor identification MACE prediction; acute coronary syndrome; admission record; hybrid model; risk factor identification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hu, D.; Huang, Z.; Chan, T.-M.; Dong, W.; Lu, X.; Duan, H. Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome. Int. J. Environ. Res. Public Health 2016, 13, 912.

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