Accepted for/Published in: JMIR Research Protocols
Date Submitted: Oct 18, 2023
Date Accepted: Apr 2, 2024
Warning: This is an author submission that is not peer-reviewed or edited.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.
Usage of machine learning in multimorbidity research: protocol for a scoping review
ABSTRACT
Background:
Multimorbidity, defined as the coexistence of multiple chronic conditions, poses significant challenges to the healthcare systems on a global scale. It is associated with increased mortality, reduced quality of life, and increased healthcare costs. The burden of multimorbidity is expected to worsen if no effective intervention is taken. Machine learning has the potential to assist in addressing these challenges, since it offers advanced analysis and decision-making capabilities, such as disease prediction, treatment development, and clinical strategies.
Objective:
This paper represents the protocol of a scoping review., which aims to identify and explore the current literature concerning the utilization of machine learning for multimorbidity patients. Furthermore, the scoping review will also explore the available literature in investigating the usability and interface aspects of machine learning models designed for patients with multimorbidity.
Methods:
The scoping review will be based on the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR). 5 databases (PubMed, EMBASE, IEEE, Web of Science, and Scopus) are chosen to conduct a literature search. Qualified studies will undergo a screening process of title, abstract, and full text. Only studies, which examines more than one chronic disease will be included in this scoping review.
Results:
The findings of the scoping review will be conveyed through a narrative synthesis. Additionally, data extracted from the studies will be formatted in a more comprehensive manner, such as charts or tables. The results will be presented in a forthcoming scoping review, which will be published in a peer-reviewed journal.
Conclusions:
This scoping review will offer insight into existing literature on machine learning in multimorbidity patients, outlining approaches and identifying research gaps.
Citation
