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Lookup NU author(s): Dr Ali HassaineORCiD, Dr Dexter CanoyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 The Author(s)The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.
Author(s): Hassaine A, Salimi-Khorshidi G, Canoy D, Rahimi K
Publication type: Article
Publication status: Published
Journal: Mechanisms of Ageing and Development
Year: 2020
Volume: 190
Print publication date: 01/09/2020
Online publication date: 06/08/2020
Acceptance date: 30/07/2020
Date deposited: 25/11/2022
ISSN (print): 0047-6374
ISSN (electronic): 1872-6216
Publisher: Elsevier Ireland Ltd
URL: https://doi.org/10.1016/j.mad.2020.111325
DOI: 10.1016/j.mad.2020.111325
PubMed id: 32768443
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