# The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria

arXiv: https://arxiv.org/abs/2207.13757 · HTML: https://mlsanity.com/paper/2207.13757/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 27 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [NeuroNER: an easy-to-use program for named-entity recognition based on neural networks](https://mlsanity.com/paper/1705.05487/) (2017)
- [CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model](https://mlsanity.com/paper/1901.07601/) (2019)
- [ITTC @ TREC 2021 Clinical Trials Track](https://mlsanity.com/paper/2202.07858/) (2022)

## Built on by (most cited first, 10 of 10)

- [LeafAI: query generator for clinical cohort discovery rivaling a human programmer](https://mlsanity.com/paper/2304.06203/) (2023)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases](https://mlsanity.com/paper/2403.17135/) (2024)
- [Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning](https://mlsanity.com/paper/2404.15320/) (2024)
- [ACR: A Benchmark for Automatic Cohort Retrieval](https://mlsanity.com/paper/2406.14780/) (2024)
- [Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review](https://mlsanity.com/paper/2505.22280/) (2025)
- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)
- [Systematic Literature Review on Clinical Trial Eligibility Matching](https://mlsanity.com/paper/2503.00863/) (2025)
- [AgriCHN: A Comprehensive Cross-domain Resource for Chinese Agricultural Named Entity Recognition](https://mlsanity.com/paper/2506.17578/) (2025)
- [The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization](https://mlsanity.com/paper/2610.18507/) (2026)

## Abstract (arXiv)

Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criteria are often most naturally described in free-text, using language familiar to clinicians and researchers. In order to identify potential participants at scale, these criteria must first be translated into queries on clinical databases, which can be labor-intensive and error-prone. Natural language processing (NLP) methods offer a potential means of such conversion into database queries automatically. However they must first be trained and evaluated using corpora which capture clinical trials criteria in sufficient detail. In this paper, we introduce the Leaf Clinical Trials (LCT) corpus, a human-annotated corpus of over 1,000 clinical trial eligibility criteria descriptions using highly granular structured labels capturing a range of biomedical phenomena. We provide details of our schema, annotation process, corpus quality, and statistics. Additionally, we present baseline information extraction results on this corpus as benchmarks for future work.