Temporal Patterns of Multiple Long-Term Conditions in Individuals with Intellectual Disability Living in Wales: An Unsupervised Clustering Approach to Disease Trajectories

Rania Kousovista, Georgina Cosma, Emeka Abakasanga, Ashley Akbari, Francesco Zaccardi +3 more
10/28/2024
cs.CYcs.AIstat.AP

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Abstract

Identifying and understanding the co-occurrence of multiple long-term conditions (MLTC) in individuals with intellectual disabilities (ID) is vital for effective healthcare management. These individuals often face earlier onset and higher prevalence of MLTCs, yet specific co-occurrence patterns remain unexplored. This study applies an unsupervised approach to characterise MLTC clusters based on shared disease trajectories using electronic health records (EHRs) from 13069 individuals with ID in Wales (2000-2021). Disease associations and temporal directionality were assessed, followed by spectral clustering to group shared trajectories. The population consisted of 52.3% males and 47.7% females, with an average of 4.5 conditions per patient. Males under 45 formed a single cluster dominated by neurological conditions (32.4%), while males above 45 had three clusters, the largest characterised circulatory (51.8%). Females under 45 formed one cluster with digestive conditions (24.6%) as most prevalent, while those aged 45 and older showed two clusters: one dominated by circulatory (34.1%), and the other by digestive (25.9%) and musculoskeletal (21.9%) system conditions. Mental illness, epilepsy, and reflux were common across groups. These clusters offer insights into disease progression in individuals with ID, informing targeted interventions and personalised healthcare strategies.

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@article{kousovista2024temporal,
  title  = {Temporal Patterns of Multiple Long-Term Conditions in Individuals with Intellectual Disability Living in Wales: An Unsupervised Clustering Approach to Disease Trajectories},
  author = {Rania Kousovista and Georgina Cosma and Emeka Abakasanga and Ashley Akbari and Francesco Zaccardi and Gyuchan Thomas Jun and Reza Kiani and Satheesh Gangadharan},
  year   = {2024},
  eprint = {2411.08894},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.08894v2}
}

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