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Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis
New Risk Associations Between Chronic Physical Illness and Mental Health Disorders revealed by Machine Learning: A Chinese Population Study
Lizhong Liang;
Tianci Liu;
William Ollier;
Yonghong Peng;
Yao Lu;
Chao Che
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.
Citation
Please cite as:
Liang L, Liu T, Ollier W, Peng Y, Lu Y, Che C
Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis