Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies. To promote these benefits, it is desired to effectively expand the scale of high-quality yet novel relational medical entity pairs that embody rich medical knowledge in a structured form. To fulfill this goal, we introduce a generative model called Conditional Relationship Variational Autoencoder (CRVAE), which can discover meaningful and novel relational medical entity pairs without the requirement of additional external knowledge. Rather than discriminatively identifying the relationship between two given medical entities in a free-text corpus, we directly model and understand medical relationships from diversely expressed medical entity pairs. The proposed model introduces the generative modeling capacity of variational autoencoder to entity pairs, and has the ability to discover new relational medical entity pairs solely based on the existing entity pairs. Beside entity pairs, relationship-enhanced entity representations are obtained as another appealing benefit of the proposed method. Both quantitative and qualitative evaluations on real-world medical datasets demonstrate the effectiveness of the proposed method in generating relational medical entity pairs that are meaningful and novel.
In the medical context, this paper describes the classic problem of "knowledge base completion" from structured data only (no text).  The authors argue for the advantages of a generative VAE approach (but without being convincing).  They do not cite the extensive literature on KB completion.  They present experimental results on their own data set, evaluating only against simpler baselines of their own VAE approach, not the pre-existing KB methods.
The authors seem unaware of a large literature on "knowledge base completion."  E.g. [Bordes, Weston, Collobert, Bengio, AAAI, 2011],  [Socher et al 2013 NIPS], [Wang, Wang, Guo 2015 IJCAI], [Gardner, Mitchell 2015 EMNLP], [Lin, Liu, Sun, Liu, Zhu AAAI 2015], [Neelakantan, Roth, McCallum 2015], 
The paper claims that operating on pre-structured data only (without using text) is an advantage.  I don't find the argument convincing.  There are many methods that can operate on pre-structured data only, but also have the ability to incorporate text data when available, e.g. "universal schema" [Riedel et al, 2014].
The paper claims that "discriminative approaches" need to iterate over all possible entity pairs to make predictions.  In their generative approach they say they find outputs by "nearest neighbor search."  But the same efficient search is possible in many of the classic "discriminatively-trained" KB completion models also.
It is admirable that the authors use an interesting (and to my knowledge novel) data set.  But the method should also be evaluated on multiple now-standard data sets, such as FB15K-237 or NELL-995.  The method is evaluated only against their own VAE-based alternatives.  It should be evaluated against multiple other standard KB completion methods from the literature, such as Jason Weston's Trans-E, Richard Socher's Tensor Neural Nets, and Neelakantan's RNNs.