Abstract
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.
M. Guo, Y. Yu and T. Wen—These authors contribute equally to this work.
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Acknowledgement
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).
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Guo, M. et al. (2018). Analysis of Disease Comorbidity Patterns in a Large-Scale China Population. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_34
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DOI: https://doi.org/10.1007/978-3-319-95933-7_34
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