Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

ABSTRACT:

Objectives

Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation.

Design

Analysis of the Get With The Guidelines-Resuscitation registry.

Setting

Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitatio

SUBMITTER: Mayampurath A 

PROVIDER: S-EPMC8810601 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature