Structural knowledge analysis and modeling of multimorbidity using graph theory based techniques

Abstract

Multimorbidity is one of the major problems in the modern medical system. The more conditions the patient has, the greater the psychological pressure will be. We propose a formal definition of the general case of Multimorbidity Disease Network detection. Based on pairwise association method, we constructed an undirected weighted graph of co-occurrence for comorbidity based on the socio-psychological profile existing in a real data set. Based on the obtained network, we used the centrality analysis of the network nodes to conduct a mesoscopic-analysis, and used the community detection algorithm to determine potential components of the network. The main results show first, that algorithms used can be helpful for extracting models of multimorbidity. Second, that aging process not only affects the number of diseases, but can also influence Multimorbidity Burden and its complexity pattern.