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.
KeywordsDisease comorbidity Complex network Network medicine
This work is partially supported by the National Natural Science Foundation of China (Nos. 61105055 and 81230086), the National Basic Research Program of China (No. 2014CB542903), the Special Programs of Traditional Chinese Medicine (Nos. 201407001, JDZX2015168, JDZX2015171 and JDZX2015170), National Key R&D Project (2017YFC1703506) and the National Key Technology R&D Programs (Nos. 2013BAI02B01 and 2013BAI13B04).
- 4.World Health Organization: ICD-10: international statistical classification of diseases and related health problems 10th rev. World Health Organ. 56(3), 65 (1992)Google Scholar
- 7.Park, J., Lee, D., Christakis, N.A., et al.: The impact of cellular networks on disease comorbidity. Mol. Syst. Biol. 5(1), 262 (2009)Google Scholar
- 10.Chaturvedi, P., Dhara, M., Arora, D.: Community detection in complex network via BGLL algorithm. Int. J. Comput. Appl. 48(1), 32–42 (2012)Google Scholar
- 14.Chen, H., Zhang, Y., Wu, D., et al.: Comorbidity in adult patients hospitalized with type 2 diabetes in northeast China: an analysis of hospital discharge data from 2002 to 2013. Biomed. Res. Int. 2016(11), 1–9 (2016)Google Scholar