EC-RAFT: Automated Generation of Clinical Trial Eligibility Criteria through Retrieval-Augmented Fine-Tuning

Eligibility criteria (EC) are critical components of clinical trial design, defining the parameters for participant inclusion and exclusion.However, designing EC remains a complex, expertise-intensive process.Traditional approaches to EC generation may fail to produce comprehensive, contextually appropriate criteria.To address these challenges, we introduce EC-RAFT, a method that utilizes Retrieval-Augmented Fine-Tuning (RAFT) to generate structured and cohesive EC directly from clinical trial titles and descriptions.EC-RAFT integrates contextual retrieval, synthesized intermediate reasoning, and fine-tuned language models to produce comprehensive EC sets.To enhance clinical alignment evaluation with referenced criteria, we also propose an LLM-guided evaluation pipeline.Our results demonstrate that our solution, which uses Llama-3.1-8B-Instruct as a base model, achieves a BERTScore of 86.23 and an EC-matched LLM-as-a-Judge score of 1.66 out of 3, outperforming zero-shot Llama-3.1 and Gemini-1.5 by 0.41 and 0.11 points, respectively.On top of that, EC-RAFT also outperforms other fine-tuned versions of Llama-3.1.EC-RAFT was trained in a lowcost setup and, therefore, can be used as a practical solution for EC generation while ensuring quality and relevance in clinical trial design.We release our code on GitHub at https://github.com/biodatlab/ec-raft/.