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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.

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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%.

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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.