Designing a new clinical trial entails many decisions, such as defining a
cohort and setting the study objectives to name a few, and therefore can
benefit from recommendations based on exhaustive mining of past clinical trial
records. Here, we propose a novel recommendation methodology, based on neural
embeddings trained on a first-of-a-kind knowledge graph of clinical trials. We
addressed several important research questions in this context, including
designing a knowledge graph (KG) for clinical trial data, effectiveness of
various KG embedding (KGE) methods for it, a novel inductive inference using
KGE, and its use in generating recommendations for clinical trial design. We
used publicly available data from clinicaltrials.gov for the study. Results
show that our recommendations approach achieves relevance scores of 70%-83%,
measured as the text similarity to actual clinical trial elements, and the most
relevant recommendation can be found near the top of list. Our study also
suggests potential improvement in training KGE using node semantics.Comment: 13 pages (w/o bibliography), 4 Figures, 6 Table