Title: Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC)
Funder: Department of Health and Social Care

Abstract: Multimorbidity is a major challenge to health systems internationally, but previous research examining how morbidities cluster within individuals has been limited in scope, has not been replicated, largely ignores social and geographical context, and has seldom translated into improvements in care. This research aims to use artificial intelligence and state-of-the art data science, social science, genomics, and health service research methods to understand clustering of morbidities within individuals, within communities, and in key clinical contexts. Objective 1: To use unsupervised and supervised machine learning including multilayer networks to examine clustering of morbidities in individuals. We will identify morbidity clusters that are consistent, operationalisable, stable, and reproducible across multiple methods and datasets. We will use these clusters in subsequent objectives along with clusters defined a priori based on theory or domain knowledge. Objective 2: To examine the genomics of morbidity clustering in individuals to support validation of objective 1 cluster solutions, and examine aetiology. We will map objective 1 clusters into UK Biobank data as traits and identify genetic loci that explain why individuals belong to one or more clusters, using polygenic models to predict an individual s cluster membership. This will underpin future mechanistic and potentially interventional research, based on mapping potential causal pathways of morbidity clustering. Objective 3: To use AI and machine learning to examine clustering of complex multimorbidity in communities and places. We will enrich existing locality data using novel geo-profiling based on news and social media free-text data, and examine how multimorbidity clusters spatially using existing social science methods for spatial analysis, and machine learning including multilayer networks. Objective 4: To develop and optimise AI tools to underpin complex interventions to reduce adverse events in people with complex multimorbidity and polypharmacy. We will improve ascertainment of serious adverse events using natural language processing to extract information from free-text data, and use knowledge graphs, and machine learning including multilayer networks to understand and predict adverse events. Quantitative modelling will be paralleled by complex intervention development work to design two interventions with AI prediction tools at their heart (one targeting people with complex multimorbidity and polypharmacy in the community, the other targeting the same people at the point of hospital admission). Our team has interdisciplinary expertise, and includes clinical and genetic researchers studying complex multimorbidity, public partners, social scientists researching wider social and spatial determinants of health and care, and informatics and data science academics with AI expertise across multiple domains including natural language processing, machine-learning including multilayer network analysis, and applied AI. We will actively collaborate with the AIM Research Support Facility and other AIM teams in order to resolve conceptual and methodological problems in this field, including through mutual reproduction of findings and reuse of methods and models. As well as conventional academic outputs, we are committed to engaging multiple stakeholders including the public, clinicians and public health professionals, managers and policymakers. We will ensure that all of our data assets are appropriately shared to be as findable, accessible, interoperable and reusable as possible.

Lay abstract: Long-term conditions are health issues which persist over years. Many people have more than one long-term condition (e.g. having both diabetes and asthma), which is usually called multimorbidity. Multimorbidity often seriously affects how well people feel and what they are able to do. However, health professionals like doctors or nurses often focus on one condition at a time. This can make it hard to ensure that care is properly joined-up and coordinated for people with multimorbidity. Research also tends to focus on one condition at a time. This is partly because people with multimorbidity can have very complicated mixes of different conditions and treatments. Artificial Intelligence (AI) uses powerful computers to analyse and understand this kind of complex information, which is carefully gathered by the NHS and others under strict rules to keep it safe and confidential. New AI methods can help us better understand what patterns of multimorbidity are common and which most affect people s lives, and help improve the quality and safety of care. We propose a programme of research which will use AI methods to: Identify the most common combinations of long-term conditions that people have, and examine whether people inherit a tendency to get particular combinations of conditions from their parents. This will help us understand what causes multimorbidity, and identify potential treatments. Examine whether particular combinations of long-term conditions are more common in some areas or communities. This will help us understand how where people live affects their health (for example, through differences in air pollution, or in access to safe open-space to play/exercise, or in having local shops that sell affordable healthy food). Develop new methods for identifying people who are likely to have unexpected health problems like falls or bleeding. This will help us to work out what changes to their treatment or care could prevent these problems. One example of a possible change is medicines review to make sure that people take the right medicines for them as an individual. Another is rapid, comprehensive assessment of people admitted to hospital to ensure that their care suits all of their needs rather than just the problem that brought them into hospital. Our team combines expertise in AI methods and clinical researchers with experience of delivering and researching healthcare for people with multiple long-term conditions. We will work closely with patients and members of the public to help us focus and interpret our research, and to help us make our findings widely known. We will work closely with other research teams to share learning and methods, and with the NHS and government to ensure that the research turns into practical improvements in care.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE