Analysis of the use of drugs for patients with COVID-19 using graph embedding

Abstract

In recent years, large-scale databases have been developed and are expected to be used in epidemiological research and safety policies for drugs in the healthcare field. Among them, the data based on medical claims, which is used in this study, are more advanced in database development in terms of ease of data structuring and comprehensiveness of medical records and target subjects. Using this database, we present a method of data mining by creating the network structure of treatment and drug combination for the purpose of analyzing similarities and relationships in the use of drugs prescribed to the patients with COVID-19. By classifying the nodes of the network based on graph embedding, evaluating the identified communities based on clinical information such as the duration of hospitalization and mortality of patients, and visualizing the communities, we will show that this method helps to understand the total picture of the treatment that was provided. This method may be applicable to diseases with various pathological symptoms and diseases for which there are many approved drugs.