We are pleased to announce that our research paper, "CReSE: Benchmark Data and Automatic Evaluation Framework for Recommending Eligibility Criteria from Clinical Trial Information", authored by Dr. Siun Kim (PhD graduate of CCADD), Jung-Hyun Won, David Lee (both current CCADD PhD students), Renqian Luo, Lijun Wu, Tao Qin (collaborator researchers at Microsoft Research Asia), and Prof. Howard Lee, has been accepted for presentation at the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024).

The paper introduces the CReSE model (Contrastive learning and Rephrasing-based and Clinical Relevance-preserving Sentence Embedding model), a new framework aimed at improving the recommendation of eligibility criteria (EC) in clinical trials. In addition, CCADD introduces a comprehensive benchmark dataset, consisting of over 3.2 million high-quality EC-title pairs from approximately 270,000 clinical trials listed on ClinicalTrials.gov.

The acceptance of our paper at EACL 2024 is a significant milestone for CCADD, demonstrating our progress in reshaping drug development through the integration of computational linguistics and interdisciplinary innovation.