Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine...

Area of Science:

• Medical Informatics
• Psychiatry
• Public Health

Background:

• The intricate relationship between chronic physical illnesses and mental health disorders is not fully understood.
• Longitudinal patterns of this comorbidity remain significantly under-researched.

Purpose of the Study:

• To employ machine learning for analyzing the complex interplay between mental health disorders and chronic physical illnesses.
• To investigate the evolving longitudinal trajectories of patient health journeys.
• To clarify comorbidity pattern variability across age and gender subgroups.

Main Methods:

• Utilized four distinct machine learning models for comprehensive analysis.
• Examined relationships between mental health disorders and chronic physical illnesses.

Main Results:

• Identified five categories of chronic physical illnesses with a higher risk of mental health comorbidity.
• Found significant correlations between specific disease combinations, notably prostate diseases and organic mental disorders (RR=2.055).
• Clarified variability in comorbidity patterns based on age and gender.

Conclusions:

• Machine learning effectively models the comorbidity between mental health and chronic physical illnesses.
• Findings offer valuable insights into disease interactions, informing targeted monitoring and management strategies.