A machine learning approach for modeling decisions in the out of hospital cardiac arrest care workflow.

ABSTRACT:

Background

A growing body of research has shown that machine learning (ML) can be a useful tool to predict how different variable combinations affect out-of-hospital cardiac arrest (OHCA) survival outcomes. However, there remain significant research gaps on the utilization of ML models for decision-making and their impact on survival outcomes. The purpose of this study was to develop ML models that effectively predict hospital's practice to perform coronary angiography (CA) in adult patients after OHCA and subsequent neurologic outcomes.

Methods

We utilized all (N = 2398) patients treated by the Chicago Fire Department Emergency Medical Services included in the Cardiac Arrest Registry to Enhance Survival (CARES) between 2013 and 2018 who survived to hospital admission to dev

SUBMITTER: Harford S 

PROVIDER: S-EPMC8787933 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature