Accepted for/Published in: JMIR Research Protocols
Date Submitted: May 14, 2025
Date Accepted: Sep 15, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Machine Learning Applications in Health Economic Evaluations: A Scoping Review Protocol
ABSTRACT
Background:
In recent years, the development of Machine Learning (ML) technologies has increased substantially, indicating the potential role of ML in transforming healthcare. 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 methods. The search will be conducted on MEDLINE (Ovid), Embase (Ovid), IEEE Xplore and Cochrane Library. The eligibility criteria of the selection process will be based on the SDMO framework approach (Study types, Data sources, Methods, Outcomes).
Results:
The database search is ongoing. 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 and challenges on using ML in health economics evaluations. Clinical Trial: osf.io/bgjmr
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Copyright
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