నైరూప్య

Recognition of Disease Comorbidity Medication Patterns Based on Network Motif Analysis

Di Chen, Jin Tian, Yuepeng Yao, Songxing Du, Jieyin Gao, Rongjuan Guo, Yun Wei and Peng Lu

Disease comorbidity is one common and important issue in medical practice. Special medical interventions will be required for patients under such conditions. Here, we proposed a network-based computation model to discover the comorbidity medication patterns by exploiting the electronic health records of a traditional Chinese medicine (TCM) hospital. One key step of this model is the estimation of three types of associations including disease-disease associations, disease-drug associations and drug-drug associations by statistical analyses. Based on these associations, a disease-drug network (DDN) was constructed. Then comorbidity medication patterns were identified from the DDN through random walk and network motif-based analysis. Taking the circulatory system diseases as examples, we applied this model to obtain the comorbidity relations among different diseases and recognize the corresponding medication patterns. As a result, we threw light on the comorbidities among different circulatory system diseases, and discovered that one disorder of the circulatory system may accompany with another lesion on the other location of the circulatory system. In addition, we identified some meaningful medication patterns which are consistent with TCM theory, for example, one blood-regulation agent alone or in accompany with another agent like a tonic can be used to treat comorbidities among different “blood stasis”-related diseases