Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis.

ABSTRACT:

Background

The efficiency of machine learning (ML) based predictive models in predicting in-hospital mortality for heart failure (HF) patients is a topic of debate. In this context, this study's objective is to conduct a meta-analysis to compare and assess existing prognostic models designed for predicting in-hospital mortality in HF patients.

Methods

A systematic search of databases was conducted, including PubMed, Embase, Web of Science, and Cochrane Library up to January 2023. To ensure comprehensiveness, we performed an additional search in June 2023. The Prediction Model Risk of Bias Assessment Tool was employed to assess the validity and reliability of ML models.

Results

Our analysis incorporated 28 studies involving a total of 106 predictive models based on 14

SUBMITTER: Yan L 

PROVIDER: S-EPMC11670054 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature