A Method for Plotting Disease Drug Analysis and Its Complications by Combining Sources of Scientific

Drugs for medical purposes aim at saving one’s life and improving their life quality. Side effects or adverse drug reactions (ADRs) on patients are studied as an important issue in pharmacology. In order to prevent the adverse drug effects, clinical trials are conducted on the drug production process, but the process of these trials is very costly and time consuming. So, various text mining methods are used to identify ADRs on scientific documents and articles. Using existing articles in the reference websites such as PubMed to predict an effective drug in the disease is a vital way to declare the drug effective. However, the effective integration of biomedical literature and biological drug network information is one of the major challenges in diagnosing a new drug. In this study, we use medical text documents to train the BioBERT model so that we can use it to discover potential drugs for treating diseases. Then, we are able to create a graphical network of drugs and their side effects with this method as well as it provides us with an opportunity to identify effective drugs that have been used in many diseases so far while having the ability to be used effectively on other diseases.