Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.

ABSTRACT:

Introduction

Machine learning (ML) has emerged as a powerful tool for uncovering patterns and generating new information. In cardiology, it has shown promising results in predictive outcomes risk assessment of heart failure (HF) patients, a chronic condition affecting over 64 million individuals globally.This scoping review aims to synthesise the evidence on ML methods, applications and economic analysis to predict the HF hospitalisation risk.

Methods and analysis

This scoping review will use the approach described by Arksey and O'Malley. This protocol will use the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol, and the PRISMA extension for scoping reviews will be used to present the results. PubMed, Scopus and Web of Science are the dat

SUBMITTER: Seringa J 

PROVIDER: S-EPMC11002361 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature