Construction of Disease Similarity Networks Using Concept Embedding and Ontology

Abstract

Discovering disease similarities are beneficial for the diagnosis and treatment of mental diseases. In this research, we proposed a data driven method, that is, integrating a variety of publicly available data resources including Unified Medical Language System (UMLS) Metathesaurus, Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT) and cui2vec concept embedding to construct a mental disease similarity network. The resulting mental disease similarity network offered a new view for navigating and investigating disease relations; it also revealed popular mental disease in the literature in terms of the number of connections and similarities with other diseases. It shows that depressive disorder is directly connected with nine other popular diseases and connects 52 other diseases in the network. The top three popular mental diseases are depressive disorder, dysthymia (now known as persistent depressive disorder), and neurosis. Future research will focus on studying the clusters generated from the similarity network.