Untangling the complexity of multimorbidity with machine learning. (September 2020)

• Record Type: Journal Article
• Authors: Hassaine, Abdelaali; Salimi-Khorshidi, Gholamreza; Canoy, Dexter; Rahimi, Kazem
• Is Part Of: Mechanisms of ageing and development. Volume 190(2020)
• Publication Date: 2020-09
• Subjects: Machine learning -- Deep learning -- Multimorbidity -- Electronic health records -- Phenotyping; Aging -- Periodicals; Developmental biology -- Periodicals; Aging -- Periodicals; Developmental Biology -- Periodicals; Vieillissement -- Périodiques; Biologie du développement -- Périodiques; Aging; Developmental biology; Periodicals; 612.67
• DOI: 10.1016/j.mad.2020.111325
• Languages: English
• ISSNs: 0047-6374

Abstract

Highlights: Machine learning is making significant contributions towards our understanding of the complex relationships between diseases. Advanced models take a range of modalities from big datasets with little pre-processing or information loss at study design. Developments in matrix factorisation and deep learning allow a better understanding of evolving patterns of multimorbidity. Abstract: 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.