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Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.


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)

SUBMITTER: Zhang PI 

PROVIDER: S-EPMC7488862 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.

Zhang Pei-I PI   Hsu Chien-Chin CC   Kao Yuan Y   Chen Chia-Jung CJ   Kuo Ya-Wei YW   Hsu Shu-Lien SL   Liu Tzu-Lan TL   Lin Hung-Jung HJ   Wang Jhi-Joung JJ   Liu Chung-Feng CF   Huang Chien-Cheng CC  

Scandinavian journal of trauma, resuscitation and emergency medicine 20200911 1


<h4>Background</h4>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.<h4>Methods</h4>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%  ...[more]

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