Eligibility criteria (EC) refer to a set of conditions an individual must meet to participate in a clinical trial, defining the study population and minimizing potential risks to patients. Previous research in clinical trial design has been primarily focused on searching for similar trials and generating EC within manual instructions, employing similarity-based performance metrics, which may not fully reflect human judgment. In this study, we propose a novel task of recommending EC based on clinical trial information, including trial titles, and introduce an automatic evaluation framework to assess the clinical validity of the EC recommendation model. Our new approach, known as CReSE (Contrastive learning and Rephrasing-based and Clinical Relevance-preserving Sentence Embedding), represents EC through contrastive learning and rephrasing via large language models (LLMs). The CReSE model outperforms existing language models pre-trained on the biomedical domain in EC clustering. Additionally, we have curated a benchmark dataset comprising 3.2M high-quality EC-title pairs extracted from 270K clinical trials available on ClinicalTrials.gov. The EC recommendation models achieve commendable performance metrics, with 49.0% precision@1 and 44.2% MAP@5 on our evaluation framework. We expect that our evaluation framework built on the CReSE model will contribute significantly to the development and assessment of the EC recommendation models in terms of clinical validity.

Linguistic Data Consortium (LDC) at the University of Pennsylvania has participated as a data provider in a variety of governmentsponsored programs that support development of Human Language Technologies. As the number of projects increases, the quantity and variety of the data LDC produces have increased dramatically in recent years. In this paper, we describe the technical infrastructure, both hardware and software, that LDC has built to support these complex, large-scale linguistic data creation efforts at LDC. As it would not be possible to cover all aspects of LDC’s technical infrastructure in one paper, this paper focuses on recent development. We also report on our plans for making our custom-built software resources available to the community as open source software, and introduce an initiative to collaborate with software developers outside LDC. We hope that our approaches and software resources will be useful to the community members who take on similar challenges.