Abstract
BackgroundDisease 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.MethodsWe 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) using correlation analysis and detected the topological patterns of disease comorbidity using both complex network and data mining methods. The comorbidity patterns were further validated by shared molecular mechanisms using disease-gene associations and pathways. To predict the disease occurrence during the whole disease progressions, we applied four machine learning methods to model the disease trajectories of patients.ResultsWe obtained the DCN with 5702 nodes and 258,535 edges, which shows a power law distribution of the degree and weight. It further indicated that there exists high heterogeneity of comorbidities for different diseases and we found that the DCN is a hierarchical modular network with community structures, which have both homogeneous and heterogeneous disease categories. Furthermore, adhering to the previous work from US and Europe populations, we found that the disease comorbidities have their shared underlying molecular mechanisms. Furthermore, take hypertension and psychiatric disease as instance, we used four classification methods to predicte the disease occurrence using the comorbid disease trajectories and obtained acceptable performance, in which in particular, random forest obtained an overall best performance (with F1-score 0.6689 for hypertension and 0.6802 for psychiatric disease).ConclusionsOur 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.
Highlights
Disease comorbidity reflects the shared molecular mechanisms or environmental factors between diseases, which would be important for improving the knowledge and management of diseases in real-world clinical settings [1,2,3]
We investigating the feasibility of predicting disease occurrence based on the comorbid trajectories of patients using four machine learning algorithms, namely Logistic Regression (LR), SVM, Random Forest (RF) and Neural Network (NN)
The average path length is 2.528 and the average Clustering coefficient (CC1) is 0.629, which indicated that Disease Comorbidity Network (DCN) is a highly clustering network, with the neighbors of a disease closely connected
Summary
Disease comorbidity reflects the shared molecular mechanisms or environmental factors between diseases, which would be important for improving the knowledge and management of diseases in real-world clinical settings [1,2,3]. It has become a major problem in treatment [4, 5], because patients with comorbidity diseases have a higher probability of hospitalization and mortality [6, 7]. When a patient suffers from multiple diseases, the treating is complicate [10] because it involves uncertainty in diagnosis and treatment. We aim to detect the general disease comorbidity patterns in Chinese populations using a large-scale clinical data set
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