Key result
Scoping review protocol outlines ML methods and economic analyses for predicting HF hospitalization risk.
Why the study?
Machine learning has shown promising results in predicting heart failure outcomes, motivating a synthesis of evidence on ML methods, applications, and economic analysis for predicting HF hospitalisation risk.
This protocol outlines a planned scoping review to synthesize current evidence on the predictive performance and cost-effectiveness of machine learning models for heart failure hospitalization risk.
Answers are grounded in this paper and cite their sources.
Scoping review will map ML methods, applications, and economics for HF hospitalization prediction; leaves open clinical adoption pending synthesis.
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 databases that will be searched. Two reviewers will independently screen the full-text studies for inclusion and extract the data. All the studies focusing on ML models to predict the risk of hospitalisation from HF adult patients will be included. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. The dissemination strategy includes peer-reviewed publications, conference presentations and dissemination to relevant stakeholders.
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Seringa et al. (2024) studied Heart failure. Machine learning models was evaluated on Machine learning models used to predict hospitalisation risk and economic analyses conducted. A planned scoping review will synthesize evidence on machine learning methods, applications, and economic analyses for predicting heart failure hospitalization risk.
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