A Word Graph-based Method for Disease Topic Identification in Biomedical Literature

An important task in biomedical literature precise search is to identify paper describing a certain disease. The tradi- tional topic identification approaches based on neural network can be used to recognize the disease topic of literature. To achieve better performance, we propose a novel word graph-based method for disease topic identification in this paper. Word graphs are constructed from literature title and abstract. Graph features are extracted and used for disease topic classification using a logistic regression or random forest classifier. Experiment results showed the word graph features outperformed disease mention frequency by a large margin. Our approach achieved better perfor- mance in identifying disease topic compared to hierarchical attention networks, which is a deep learning approach for document classification. We also demonstrated the use of the proposed method in identifying the disease topic in an application scenario.