ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
A Word Graph-based Method for Disease Topic Identification in Biomedical Literature.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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32477690PMC7233094What OpenQuestion holds
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