Analysis of Disease Comorbidity Patterns in a Large-Scale China Population

Background: Disease comorbidity is popular and has significant indications for disease progress and management. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set. Materials and Methods: We extracted the diseases from a large-scale anonymized data set derived from 8,572,137 inpatients in 453 hospitals across China. We built a Disease Comorbidity Network (DCN) with significant disease co-occurrence and detected the topological patterns of disease comorbidity using both complex network and data mining methods. Results: We obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It indicated that there exists high heterogeneity of comorbidities for different diseases. Meanwhile, we found that the DCN is a hierarchical modular network with community structures. We further divided the network into 10 modules using community detection algorithm, which showed two types of modules exist in the DCN. Conclusions: Our study indicates that disease comorbidity is significant and valuable to understand the disease incidences and their interactions in real-world populations, which will provide important insights for detection of the patterns of disease classification, diagnosis and prognosis.