Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
Zifeng Wang, Jimeng Sun · 2022
Clinical trials are essential for drug development but are extremely expensive and timeconsuming 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, called Trial2Vec, which learns through self-supervision without the need for 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 1 ) 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 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 pretrained embeddings benefit the downstream trial outcome prediction task over 240k trials. 2