AUTHOR=Woodman Richard John , Koczwara Bogda , Mangoni Arduino Aleksander 

TITLE=Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1302844

DOI=10.3389/fmed.2023.1302844

ISSN=2296-858X

ABSTRACT=The current management of patients with multimorbidity is suboptimal with either a single-disease approach to care or treatment guideline adaptations that result in poor adherence due to their complexity. Although this has resulted in calls for more holistic and personalised approaches to prescribing, progress towards these goals has remained slow. With the rapid advancement of machine learning (ML) methods, promising approaches now also exist to accelerate the advance of precision medicine in multimorbidity. These include analysing disease comorbidity networks, using knowledge graphs that integrate knowledge from different medical domains, and the application of network analysis and graph ML. Multimorbidity disease networks have been used to improve disease diagnosis, treatment recommendation and patient prognosis. Knowledge graphs that combine different medical entities connected by multiple relationship types integrate data from different sources, allowing for complex interactions and creating a continuous flow of information. Network analysis and graph ML can then extract the topology and structure of networks and reveal hidden properties including disease phenotypes, network hubs and pathways, predict drugs for repurposing, and determine safe and more holistic treatments. In this paper, we describe the basic concepts of creating bipartite and unipartite disease and patient networks, and review the use of knowledge graphs, graph algorithms, graph embedding methods, and graph ML within the context of multimorbidity. Specifically, we provide an overview of the application of graph theory for studying multimorbidity, the methods employed to extract knowledge from graphs, and examples of the application of disease networks for determining the structure and pathways of multimorbidity, identifying disease phenotypes, predicting health outcomes, and selecting safe and effective treatments. In today's modern data-hungry ML focused world, such network-based techniques are likely to be at the forefront in developing robust clinical decision support tools for safer and more holistic approaches to treating older patients with multimorbidity.