Cluster and Trajectory Analysis of Multiple Long-Term Conditions in Adults with Learning Disabilities | Springer Nature Link

Cluster and Trajectory Analysis of Multiple Long-Term Conditions in Adults with Learning Disabilities | Springer Nature Link
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Cluster and Trajectory Analysis of Multiple Long-Term Conditions in Adults with Learning Disabilities
Conference paper
First Online:
15 August 2024
pp 3–16
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Artificial Intelligence in Healthcare
(AIiH 2024)
Abstract
Individuals with learning disabilities (LD) are at a heightened risk of experiencing multiple long-term conditions (MLTCs) due to various factors, which can lead to increased premature mortality rates and compromised quality of life. Despite this, there is limited research employing cluster analysis to identify and categorise similar patterns of MLTCs in patients with learning disabilities. This study applies machine learning clustering algorithms to data from 13,069 adults with learning disabilities in Wales, using a 3-cluster Gaussian Mixture Model for 6,830 males and a 3-cluster BIRCH algorithm for 6,239 females. Cluster 3 for males and Cluster 1 for females represented ‘relatively healthy’ groups, characterised by predominantly younger patients with lower MLTC counts and lower hospitalization rates. However, these clusters exhibited the lowest age at mortality, 62 years for males and approximately 65 years for females, indicating a higher likelihood of preventable deaths. Subsequently, prevalent combinations of MLTCs and common disease trajectories were analysed within these clusters. Identifying distinct MLTC clusters, prevalent combinations, and trajectories provides crucial insights for optimizing care pathways, targeted interventions, and resource allocation tailored to the specific needs of individuals with learning disabilities. This ultimately aims to improve health outcomes and quality of life for this population.
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Acknowledgments
Data-driven machinE-learning aided stratification and management of multiple long-term COnditions in adults with intellectual disabilitiEs (DECODE) project (NIHR203981) is funded by the NIHR AI for Multiple Long-term Conditions (AIM) Programme. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work uses data provided by patients and collected by the NHS as part of their care and support. We also want to acknowledge all data providers who make anonymised data available for research.
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Authors and Affiliations
Department of Computer Science, School of Science, Loughborough University, Loughborough, UK
Emeka Abakasanga, Rania Kousovista & Georgina Cosma
School of Design and Creative Arts, Loughborough University, Loughborough, UK
Gyuchan Thomas Jun
Leicestershire Partnership NHS Trust, Leicester, UK
Reza Kiani & Satheesh Gangadharan
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Emeka Abakasanga
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Rania Kousovista
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Georgina Cosma
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Gyuchan Thomas Jun
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Reza Kiani
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Satheesh Gangadharan
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Georgina Cosma
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Editors and Affiliations
Swansea University, Swansea, UK
Xianghua Xie
Queen's University, Belfast, UK
Iain Styles
Swansea University, Swansea, UK
Gibin Powathil
University of Rome Tor Vergata, Rome, Italy
Marco Ceccarelli
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Abakasanga, E., Kousovista, R., Cosma, G., Jun, G.T., Kiani, R., Gangadharan, S. (2024). Cluster and Trajectory Analysis of Multiple Long-Term Conditions in Adults with Learning Disabilities.

In: Xie, X., Styles, I., Powathil, G., Ceccarelli, M. (eds) Artificial Intelligence in Healthcare. AIiH 2024. Lecture Notes in Computer Science, vol 14976. Springer, Cham. https://doi.org/10.1007/978-3-031-67285-9_1
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15 August 2024
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Keywords
Cluster analysis
Trajectories
Learning disability
Multiple long-term conditions
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