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Machine Learning in Health Economic Evaluations: Protocol for a Scoping Review
Hanan Daghash1,2, Ashleigh Kernohan1, Rosiered Brownson-Smith2
1Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom.
Background:
In recent years, the development of machine learning (ML) applications has increased substantially, indicating the potential role of ML in transforming health care. However, the integration of ML approaches into health economic evaluations is underexplored and has several challenges.
Objective:
This scoping review aims to explore the applications of ML in health economic evaluations. This review will also seek to identify some potential challenges to the use of ML in health economic evaluations.
Methods:
This review will use PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methods. The search will be conducted on MEDLINE (Ovid), Embase (Ovid), IEEE Xplore, and Cochrane Library databases. The eligibility criteria of the selection process will be based on the study types, data sources, methods, and outcomes (SDMO) framework approach.
Results:
The database search yielded 4141 records after removal of retractions and duplicates. Title and abstract screening of 3718 records has been completed, resulting in 30 reports retrieved for eligibility assessment. Data extraction and charting are currently in progress. The results will be published in peer-reviewed journals by the end of 2025.
Conclusions:
This review will help to build up the current understanding of how ML applications are integrated in health economics evaluations. This will also explore the potential barriers to and challenges of using ML in health economics evaluations.
International Registered Report Identifier (Irrid):
DERR1-10.2196/77494.
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