Accepted for/Published in: JMIR AI
Date Submitted: Feb 13, 2025
Open Peer Review Period: Feb 13, 2025 - Apr 10, 2025
Date Accepted: Apr 1, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
New Risk Associations Between Chronic Physical Illness and Mental Health Disorders revealed by Machine Learning: A Chinese Population Study
ABSTRACT
The mechanisms underlying the mutual relationships between chronic physical illnesses and mental health disorders, which potentially explain their association, remain unclear. Furthermore, how patterns of this comorbidity evolve over time are under-investigated significantly. Here, four Machine Learning models were used to model and analyze the intricate interplay between mental health disorders and chronic physical illnesses. This analysis facilitated an investigation of evolving longitudinal trajectories of patients' “health journeys”. We show that five categories of chronic physical illnesses exhibit a higher risk of comorbidity with mental health disorders. Further analysis of the intensity of comorbidity revealed evidence for correlations between disease combinations. The highest intensity of comorbidity strength was seen between prostate diseases and Organic Mental Disorders (RR = 2.055, Φ = 0.212). Finally, by analyzing the effects of age and gender in different patient population sub-groups, we clarified the variability of comorbidity patterns within the patient population.
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