Generating Medical Hypotheses Based on Evolutionary Medical Concepts

Literature based discovery (LBD) is a task that aims to uncover hidden associations between non-interacting scientific concepts by rationally connecting independent nuggets of information. Broadly, prior approaches to LBD include use of: a) distributional statistics and explicit representation, b) graph-theoretic measures, and c) supervised machine learning methods to find associations. However, purely distributional approaches may not necessarily entail semantically meaningful association and graph-theoretic approaches suffer from scalability issues. While supervised machine learning based approaches have the potential to elucidate associations, the training data required is too expensive to generate. In this paper we propose a novel dynamic Medical Subject Heading (MeSH) embedding model which is able to model the evolutionary behavior of medical concepts to uncover latent associations between them. The proposed model allows us to learn the evolutionary trajectories of MeSH embeddings and detect informative terms. Hence, based on the dynamic MeSH embeddings, meaningful medical hypotheses can be efficiently generated. To evaluate the efficacy of the proposed model, we perform both qualitative and quantitative evaluation. The results demonstrate that leveraging the evolutionary features of MeSH concepts is an effective way for predicting novel associations.