Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision

• URL: http://arxiv.org/abs/2206.14719v1
• Date: Wed, 29 Jun 2022 15:37:11 GMT
• Title: Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision
• Authors: Zifeng Wang and Jimeng Sun
• Abstract summary: We propose Trial2Vec, which learns through self-supervision without annotating similar clinical trials. meta-structure of trial documents (e.g., title, eligibility criteria, target disease) along with clinical knowledge are leveraged to automatically generate contrastive samples. 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.
• Score: 42.859662256134584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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