Cluster and Trajectory Analysis of Multiple Long-Term Conditions in Adults with Learning Disabilities

Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Healthcare - 1st International Conference, AIiH 2024, Proceedings
EditorsXianghua Xie, Gibin Powathil, Iain Styles, Marco Ceccarelli
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-16
Number of pages14
ISBN (Print)9783031672842
DOIs
StatePublished - 1 Jan 2024
Externally publishedYes
Event1st International Conference on Artificial Intelligence in Healthcare, AIiH 2024 - Swansea, United Kingdom
Duration: 4 Sep 20246 Sep 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14976 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Artificial Intelligence in Healthcare, AIiH 2024
Country/TerritoryUnited Kingdom
CitySwansea
Period4/09/246/09/24

Keywords

  • Cluster analysis
  • Learning disability
  • Multiple long-term conditions
  • Trajectories

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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