Machine-Learning for Phenotyping and Prognostication of Myocardial Infarction and Injury in Suspected Acute Coronary Syndrome.

ABSTRACT:

Background

Clinical work-up for suspected acute coronary syndrome (ACS) is resource intensive.

Objectives

This study aimed to develop a machine learning model for digitally phenotyping myocardial injury and infarction and predict 30-day events in suspected ACS patients.

Methods

Training and testing data sets, predominantly derived from electronic health records, included suspected ACS patients presenting to 6 and 26 South Australian hospitals, respectively. All index presentations and 30-day death and myocardial infarction (MI) were adjudicated using the Fourth Universal Definition of MI. We developed 2 diagnostic prediction models which phenotype myocardial injury and infarction according to the Fourth UDMI (chronic myocardial injury vs acute myocardial injury patter

SUBMITTER: Khan E 

PROVIDER: S-EPMC11450946 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature