Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis

Liang, Lizhong ORCID: https://orcid.org/0009-0003-2501-3633, Liu, Tianci ORCID: https://orcid.org/0009-0007-9655-4719, Ollier, William ORCID: https://orcid.org/0000-0001-6502-6584, Peng, Yonghong ORCID: https://orcid.org/0000-0002-5508-1819, Lu, Yao ORCID: https://orcid.org/0000-0001-9004-9569 and Che, Chao ORCID: https://orcid.org/0000-0003-2978-5430 (2025) Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis. JMIR AI, 4. e72599. ISSN 2817-1705

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Abstract

Background: 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 significantly underinvestigated. Objective: The main aim of this study was to use machine learning models to model and analyze the complex interplay between mental health disorders and chronic physical illnesses. Another aim was to investigate the evolving longitudinal trajectories of patients’ “health journeys.” Moreover, the study intended to clarify the variability of comorbidity patterns within the patient population by considering the effects of age and gender in different patient subgroups. Methods: Four machine learning models were used to conduct the analysis of the relationship between mental health disorders and chronic physical illnesses. Results: Through systematic research and in-depth analysis, we found that 5 categories of chronic physical illnesses exhibit a higher risk of comorbidity with mental health disorders. Further analysis of comorbidity intensity revealed correlations between specific disease combinations, with the strongest association observed between prostate diseases and organic mental disorders (relative risk=2.055, Φ=0.212). Additionally, by examining patient subgroups stratified by age and gender, we clarified the variability of comorbidity patterns within the population. These findings highlight the complexity of disease interactions and emphasize the need for targeted monitoring and comprehensive management strategies in clinical practice. Conclusions: Machine learning models can effectively be used to study the comorbidity between mental health disorders and chronic physical illnesses. The identified high-risk chronic physical illness categories for comorbidity, the correlations between disease combinations, and the variability of comorbidity patterns according to age and gender provide valuable insights into the complex relationship between these two types of disorders.