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) < 1 month and all-cause mortalit

SUBMITTER: Zhang PI 

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

REPOSITORIES: biostudies-literature