JMIR Publications

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. More precisely, the objective is to recognize various machine learning models, the patient groups involved, features considered, types of input data, the maturity of the machine learning algorithms, and the outcomes from these machine learning models.

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. Two reviewers will independently screen the titles, abstract, and full text of identified studies based on predefined eligibility criteria. Covidence will be used a tool for managing and screening articles. Only studies, which examines more than one chronic disease or individuals with a single chronic condition at risk of developing another will be included in the scoping review. Data from the included studies will be collected using Microsoft Excel. The focus on the data extraction will be on bibliographical information, objective, study population, type of input data, type of algorithm, performance, maturity of the algorithms, and outcome.

Results:

The screening process will be presented in a Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) flow diagram. 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:

To our knowledge, this may be the first scoping review to investigate the usage of machine learning in multimorbidity research. The goal of the scoping review is to summarize the field of literature on machine learning in patients with multiple chronic conditions, highlighting different approaches, and potentially discovering research gaps. The results will offer insights for future research within this field, contributing to developments that can enhance patient outcomes.