Liang Lizhong, Liu Tianci, Ollier William, Peng Yonghong, Lu Yao, Che Chao
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Affiliated Hospital of Guangdong Medical College Hospital, Zhanjiang, China.
JMIR AI. 2025 Jun 30;4:e72599. doi: 10.2196/72599.
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.
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.
Four machine learning models were used to conduct the analysis of the relationship between mental health disorders and chronic physical illnesses.
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.
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.
慢性躯体疾病与精神健康障碍之间相互关系的潜在机制尚不清楚，而这些机制可能解释了它们之间的关联。此外，这种共病模式随时间的演变情况也未得到充分研究。
本研究的主要目的是使用机器学习模型对精神健康障碍与慢性躯体疾病之间的复杂相互作用进行建模和分析。另一个目的是研究患者“健康历程”不断演变的纵向轨迹。此外，该研究旨在通过考虑不同患者亚组中年龄和性别的影响，阐明患者群体中共病模式的变异性。
使用四种机器学习模型对精神健康障碍与慢性躯体疾病之间的关系进行分析。
通过系统研究和深入分析，我们发现5类慢性躯体疾病与精神健康障碍共病的风险较高。对共病强度的进一步分析揭示了特定疾病组合之间的相关性，其中前列腺疾病与器质性精神障碍之间的关联最强（相对风险=2.055，Φ=0.212）。此外，通过检查按年龄和性别分层的患者亚组，我们阐明了人群中共病模式的变异性。这些发现凸显了疾病相互作用的复杂性，并强调了临床实践中进行针对性监测和综合管理策略的必要性。
机器学习模型可有效用于研究精神健康障碍与慢性躯体疾病之间的共病情况。所确定的共病高风险慢性躯体疾病类别、疾病组合之间的相关性以及根据年龄和性别划分的共病模式变异性，为这两类疾病之间的复杂关系提供了有价值的见解。
