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Untangling the complexity of multimorbidity with machine learning

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.

Publication metadata

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


DOI: 10.1016/j.mad.2020.111325

PubMed id: 32768443


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Funder referenceFunder name
KR and DC received support from the British Heart Foundation grant ref: PG/18/65/33872.
PEAK Urban programme, funded by UKRI’s Global Challenge Research Fund, Grant Ref: ES/P011055/1.