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.

近年来，多种疾病的患病率不断增加，给医疗保健提供和服务带来了重大负担。事实证明，了解其决定因素和影响是一项挑战，但它为研究提供了超越孤立疾病研究的新机会。在本文中，我们回顾了机器学习领域如何提供许多工具来解决多发病的研究挑战。我们重点介绍了矩阵分解、深度学习和拓扑数据分析等有前途的方法的最新进展，以及这些方法如何使多发病研究超越横断面、专家驱动或验证性方法，以更好地了解多发病的演变模式。我们讨论了机器学习的挑战和机遇，以确定以前知之甚少的疾病关联之间可能存在的因果关系，同时对此类关联的不确定性进行估计。最后，我们总结了机器学习研究工具在更广泛的临床应用中面临的一些挑战，并提出了一些解决方案。