Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study.

ABSTRACT:

Background

The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention.

Objective

The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness.

Methods

This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms wit

SUBMITTER: Hong S 

PROVIDER: S-EPMC7435618 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature