TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching

This repository contains the supplementary materials for the paper titled "TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching." It includes:

Synthetic patient dataset used for evaluation (Ideal Candidates)

Fine-tuned Large Language Models (LLMs) for tasks: named entity recognition (NER), trial re-ranking, and Chain-of-Thought (CoT) reasoning

Training and fine-tuning data used to develop the models

Matching results on synthetic patient profiles (Ideal Candidates + TREC 2021 & 2022)

Normalization dictionaries used for standardizing the extracted biomedical entities

These resources are provided to facilitate reproducibility and further research on AI-driven clinical trial matching systems.