Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment-implications for clinical practice.

ABSTRACT:

Background

Artificial intelligence (AI)-based models can augment clinical decision-making, including prediction, diagnosis, and treatment, in all aspects of medicine.

Research questions

The current systematic review aims to provide a summary of existing data about the role of machine learning (ML) techniques in predicting in-hospital cardiac arrest, life-threatening ventricular arrhythmias, and respiratory arrest.

Methods

The study was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) framework. PubMed, Embase, and Web of Science without any restriction were searched to extract relevant manuscripts until October 20, 2023. Additionally, the reference list of all potential studies was searched to identify furthe

SUBMITTER: Thambiraj G 

PROVIDER: S-EPMC12549753 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature