Matching patients to clinical trials with large language models
Qiao Jin1, Zifeng Wang2, Charalampos S Floudas3
1National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, USA.
Nature Communications
|November 18, 2024
Summary
TrialGPT, a new framework using large language models, significantly improves patient-to-trial matching. It efficiently identifies suitable clinical trials, reducing recruitment time by over 40%.
Area of Science:
- Clinical Informatics
- Artificial Intelligence in Medicine
- Biomedical Data Science
Background:
- Patient recruitment is a major bottleneck in clinical trials.
- Efficiently matching patients to suitable trials is crucial for research advancement.
- Current methods for patient-trial matching are often time-consuming and labor-intensive.
Purpose of the Study:
- To introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching using large language models.
- To develop and evaluate a system that automates and enhances the accuracy of identifying eligible clinical trials for patients.
- To assess the efficiency and effectiveness of TrialGPT in reducing patient recruitment screening time.
Main Methods:
- Developed TrialGPT, a framework with three modules: TrialGPT-Retrieval for large-scale trial filtering, TrialGPT-Matching for criterion-level patient eligibility prediction, and TrialGPT-Ranking for generating trial-level scores.
- Evaluated the framework on three synthetic patient cohorts with extensive trial annotations.
- Conducted manual evaluations of patient-criterion pairs and a user study to assess performance and impact on screening time.
Main Results:
- TrialGPT-Retrieval recalled over 90% of relevant trials while processing less than 6% of the initial collection.
- TrialGPT-Matching achieved 87.3% accuracy in predicting patient eligibility, with explanations comparable to expert performance.
- TrialGPT-Ranking scores showed high correlation with human judgments, outperforming competing models by 43.8% in ranking and exclusion tasks.
- A user study demonstrated that TrialGPT reduced screening time by 42.6%.
Conclusions:
- TrialGPT offers a powerful and efficient solution for zero-shot patient-to-trial matching.
- The framework significantly enhances the accuracy and speed of identifying suitable clinical trials for patients.
- TrialGPT presents a promising advancement for optimizing patient recruitment in clinical research.
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