  Clinical trials are essential for drug development but are extremely
expensive and time-consuming to conduct. It is beneficial to study similar
historical trials when designing a clinical trial. However, lengthy trial
documents and lack of labeled data make trial similarity search difficult. We
propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns
through self-supervision without annotating similar clinical trials.
Specifically, the meta-structure of trial documents (e.g., title, eligibility
criteria, target disease) along with clinical knowledge (e.g., UMLS knowledge
base https://www.nlm.nih.gov/research/umls/index.html) are leveraged to
automatically generate contrastive samples. Besides, Trial2Vec encodes trial
documents considering meta-structure thus producing compact embeddings
aggregating multi-aspect information from the whole document. We show that our
method yields medically interpretable embeddings by visualization and it gets a
15% average improvement over the best baselines on precision/recall for trial
retrieval, which is evaluated on our labeled 1600 trial pairs. In addition, we
prove the pre-trained embeddings benefit the downstream trial outcome
prediction task over 240k trials. Software ias available at
https://github.com/RyanWangZf/Trial2Vec.
