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Machine Learning in Health Economic Evaluations: A Scoping Review Protocol
Hanan Daghash;
Ashleigh Kernohan;
Rosiered Brownson-Smith;
Rohan Pandey;
Ananya Ananthakrishnan;
Cen Cong;
Victoria Riccalton;
Edward Meinert;
Gurdeep S Sagoo
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
Citation
Please cite as:
Daghash H, Kernohan A, Brownson-Smith R, Pandey R, Ananthakrishnan A, Cong C, Riccalton V, Meinert E, Sagoo GS
Machine Learning in Health Economic Evaluations: Protocol for a Scoping Review