Title: DECODE: Data-driven machinE-learning aided stratification and management of multiple long-term COnditions in adults with intellectual disabilitiEs
Funder: Department of Health and Social Care

Abstract: Research question: What actionable insights can be developed from machine learning aided analysis of &ldquo;multiple long term conditions in people with Intellectual Disability&rdquo; to support a model of effective coordination of care? Background: Approximately 1% of people identified in general practice have ID and of these, around two-thirds have two or more long-term conditions. Physical ill-health symptoms in this population are often mistakenly attributed to either a mental health/behavioural problem or as being inherent to the person s ID which contributes to health inequalities. A principal manner in which support could be improved involves care coordination, which is defined as joined-up care between health and social services. However, the lack of ability to understand/predict the complex interactions between MLTCs and care needs of individuals makes it challenging to provide effective coordination of person-centred and holistic care. Aim: To apply machine learning approaches to identify clusters and trajectories of MLTCs in people with ID and to utilise them to develop actionable insights and practical usage scenarios for effective care coordination to improve the health and wellbeing of people with ID. Objectives: Our research is organised into six objectives: To mine and clean data from two independent data sources and platforms: the Clinical Practice Research Datalink (CPRD) for England and the Secure Anonymised Information Linkage (SAIL) Databank for Wales. To identify the clusters of MLTCs that exist in people with ID using appropriate machine learning approaches. To analyse the trajectories of the most dominant/important clusters and their interactions with risk factors and outcomes. To develop an approach to combine multiple clinical guidelines relevant to the dominant/important clusters. To design user-friendly visualisations that present the outputs of AI analytics in a transparent, meaningful and trusted way to professionals, people with ID and carers. To develop actionable insights and practical usage scenarios of AI with/for multiple stakeholders for the effective coordination of holistic care. Methods: WP1: Data mining/cleaning/bias mitigation. WP2: statistical analysis, hierarchical and non-hierarchical computational clustering methods, feature selection algorithms, deep learning classification methods, deep recurrent neural networks, computer-interpretable transition-based medical recommendation model, and AI argumentation methodology. WP3: user requirements analysis, prototyping and front-end design. WP4: reflective semi-structured interviews and stakeholder workshops (participatory design and World Caf&eacute; approach) Timeline for delivery: Month6: protocol/ethical approval, data mining/cleaning. Month12: MLTCs clustering and visualisation. Month18: risk factors analysis, temporal clustering and visualisation. Month24: advanced visual toolkit and system analysis of care coordination. Month30: actionable insights and practical usage scenarios. Anticipated impact and dissemination: Influence shaping policy and guidelines at the national level with support from National Learning Disability Professional Senate and our additional links to Royal College of Psychiatrists, Health Education England, Public Health Wales, NHS England and NHS Wales. Disseminate the outputs with support from our collaborators including Learning Disability Professional Senate and Learning Disability England. Further develop the proof of concept of practical usage scenarios, with the support of appropriate networks like AHSN, through piloting and adoption across services in the UK and beyond.

Lay abstract: Background to the research About 1 in 100 people are identified as having intellectual disabilities. Of these, about two-thirds have two or more long-term health problems, known as multiple long-term conditions (MLTCs). Sometimes these conditions can be delayed or prevented through lifestyle changes, such as diabetes and heart problems. For other conditions like epilepsy, better management can improve the quality of life. Aims of the research We want to use computer programs, called machine learning, to learn more about MLTCs in people with intellectual disabilities. We will find out which conditions are more likely to occur together and what happens over time. This information will help us to develop a new joined-up model of care for people with intellectual disabilities and MLTCs. Design and methods used Our research combines healthcare information (data) and what we hear from experts. The experts include experts by experience (people with intellectual disabilities and their carers) and professional experts. 1. Healthcare data We will use healthcare data on people with intellectual disabilities from England and Wales to find out: MLTCs that tend to occur together, and What happens to some of these MLTCs over time and the role of factors, like lifestyle habits of people and their financial and social situations. 2. Input from stakeholders Stakeholders will either act as research partners (PPI groups) or as research participants. We will recruit research participants for workshops or interviews where this is needed separately. They will help to: Decide on the most important MLTCs affecting the lives of people with intellectual disabilities. Make recommendations about the care of people with MLTCs. Produce visual information such as graphs and infographics specially designed for people that will use them, including people with intellectual disabilities. When we have finished this work, we will propose a new model which aims to improve care and support for people with intellectual disabilities and MLTCs. Patient and public involvement We have worked closely with people with intellectual disabilities and their carers to develop this project. We are currently training a group of people with intellectual disabilities to become research partners. Our work with the groups helped us understand the support they need to take part in the research. Continued involvement (see PPI section) will be supported by our carer co-applicant, charity partners and Sense College, a local college supporting people with intellectual disabilities. Dissemination As well as research publications, our findings will be shared via social media, our charity partners, intellectual disability conferences, and national committees that contribute to guidelines. Ultimately, we are committed to valuing diversity and improving the lives of people with intellectual disabilities to manage and prevent MLTCs.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: MENTAL HEALTH;GENERIC HEALTH RELEVANCE