Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain
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ORIGINAL RESEARCH
Open Access
Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain Pei-I Zhang1†, Chien-Chin Hsu2,3†, Yuan Kao2,4, Chia-Jung Chen5, Ya-Wei Kuo6, Shu-Lien Hsu7, Tzu-Lan Liu5, Hung-Jung Lin2,8, Jhi-Joung Wang9,10, Chung-Feng Liu9 and Chien-Cheng Huang2,11,12*
Abstract Background: A big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it. Methods: In total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) < 1 month and all-cause mortality < 1 month. Comparisons of the predictive accuracies among random forest, logistic regression, support-vector clustering (SVC), and K-nearest neighbor (KNN) models were also performed. Results: Predicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI < 1 month and 0.999 for all-cause mortality < 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI < 1 month and 0.888 for all-cause mortality < 1 month. Conclusions: An AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted. Keywords: Artificial intelligence, Chest pain, Emergency department, Machine learning, Major adverse cardiac events
* Correspondence: [email protected] † Pei-I Zhang and Chien-Chin Hsu contributed equally to this work. 2 Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City 710, Taiwan 11 Department of Senior Services, Southern Taiwan University of Science and Technology, Tainan, Taiwan Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unle
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