Clinical Trial Retrieval via Multi-grained Similarity Learning

Clinical trial analysis is one of the main business directions and services in IQVIA, and reviewing past similar studies is one of the most critical steps before starting a commercial clinical trial. The current review process is manual and time-consuming, requiring a clinical trial analyst to manually search through an extensive clinical trial database and then review all candidate studies. Therefore, it is of great interest to develop an automatic retrieval algorithm to select similar studies by giving new study information. To achieve this goal, we propose a novel group-based trial similarity learning network named GTSLNet, consisting of two kinds of similarity learning modules. The pair-wise section-level similarity learning module aims to compare the query trial and the candidate trial from the abstract semantic level via the proposed section transformer. Meanwhile, a word-level similarity learning module uses the word similarly matrix to capture the low-level similarity information. Additionally, an aggregation module combines these similarities. To address potential false negatives and noisy data, we introduce a variance-regularized group distance loss function. Experiment results show that the proposed GTSLNet significantly and consistently outperforms state-of-the-art baselines.