Abstract
Diabetes mellitus and obesity are becoming some of the most serious public health challenges in the world. To help researchers more quickly reveal the complex relationships existing between diabetes mellitus, obesity, and related diseases in the literature, and give them an inspiration to search the effective treatments for these diseases, we propose a novel model named as representative latent Dirichlet allocation topic model (RLDA). We conducted the representation learning model on more than 337,000 pieces of diabetes and obesity related literature published in the recent decade. Then, an explicit analysis of the final result using a series of visualization tools to discover meaningful relations among diabetes mellitus, obesity, and other diseases was performed. In order to show the credibility of our discoveries, we used clinical reports, such as Standards of Medical Care in Diabetes, which were not used in our training data, to verify our results. Fortunately, a sufficient number of the reports were direct matches. With the help of our model, we achieved satisfactory results for diabetes mellitus and obesity. For example, we discovered that 22 other diseases are closely related to diabetes mellitus, 10 with obesity and 8 with both. In addition, the tumor, adolescent/child, inflammation, and hypertension will be the hottest research topics relating to diabetes and obesity in the near future. We believe that the representational learning model we have built can help biomedical researchers direct the focus and adjust the direction of their work.