On the Generative Discovery of Structured Medical Knowledge

Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce medical information access cost for both individuals and societies. However, expanding the scale of high-quality yet structured medical knowledge usually comes with tedious efforts in data preparation and human annotation. To promote the benefits while minimizing the data requirement in expanding medical knowledge, we introduce a generative perspective to study the relational medical entity pair discovery problem. A generative model named Conditional Relationship Variational Autoencoder is proposed to discover meaningful and novel medical entity pairs by purely learning from the expression diversity in the existing relational medical entity pairs. Unlike discriminative approaches where high-quality contexts and candidate medical entity pairs are carefully prepared to be examined by the model, the proposed model generates novel entity pairs directly by sampling from a learned latent space without further data requirement. The proposed model explores the generative modeling capacity for medical entity pairs while incorporating deep learning for hands-free feature engineering. It is not only able to generate meaningful medical entity pairs that are not yet observed, but also can generate entity pairs for a specific medical relationship. The proposed model adjusts the initial representations of medical entities by addressing their relational commonalities. Quantitative and qualitative evaluations on real-world relational medical entity pairs demonstrate the effectiveness of the proposed method in generating relational medical entity pairs that are meaningful and novel.