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Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

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 typi...

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Autores principales: Hu, Danqing, Huang, Zhengxing, Chan, Tak-Ming, Dong, Wei, Lu, Xudong, Duan, Huilong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036745/
https://www.ncbi.nlm.nih.gov/pubmed/27649220
http://dx.doi.org/10.3390/ijerph13090912
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author Hu, Danqing
Huang, Zhengxing
Chan, Tak-Ming
Dong, Wei
Lu, Xudong
Duan, Huilong
author_facet Hu, Danqing
Huang, Zhengxing
Chan, Tak-Ming
Dong, Wei
Lu, Xudong
Duan, Huilong
author_sort Hu, Danqing
collection PubMed
description 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.
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spelling pubmed-50367452016-09-29 Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome Hu, Danqing Huang, Zhengxing Chan, Tak-Ming Dong, Wei Lu, Xudong Duan, Huilong Int J Environ Res Public Health Article 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. MDPI 2016-09-13 2016-09 /pmc/articles/PMC5036745/ /pubmed/27649220 http://dx.doi.org/10.3390/ijerph13090912 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hu, Danqing
Huang, Zhengxing
Chan, Tak-Ming
Dong, Wei
Lu, Xudong
Duan, Huilong
Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome
title Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome
title_full Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome
title_fullStr Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome
title_full_unstemmed Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome
title_short Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome
title_sort utilizing chinese admission records for mace prediction of acute coronary syndrome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036745/
https://www.ncbi.nlm.nih.gov/pubmed/27649220
http://dx.doi.org/10.3390/ijerph13090912
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