Matching Patients to Clinical Trials with Large Language Models.
| العنوان: | Matching Patients to Clinical Trials with Large Language Models. |
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| المؤلفون: | Jin Q; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)., Wang Z; Department of Computer Science, University of Illinois Urbana-Champaign., Floudas CS; Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health., Chen F; School of Medicine, University of Pittsburgh., Gong C; Jacob Medical Center, Albert Einstein College of Medicine., Bracken-Clarke D; Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health., Xue E; Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health., Yang Y; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH).; School of Computer Science, University of Maryland College Park., Sun J; Department of Computer Science, University of Illinois Urbana-Champaign., Lu Z; National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH). |
| المصدر: | ArXiv [ArXiv] 2024 Apr 27. Date of Electronic Publication: 2024 Apr 27. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101759493 Publication Model: Electronic Cited Medium: Internet ISSN: 2331-8422 (Electronic) Linking ISSN: 23318422 NLM ISO Abbreviation: ArXiv Subsets: PubMed not MEDLINE |
| مستخلص: | Clinical trials are often hindered by the challenge of patient recruitment. In this work, we introduce TrialGPT, a first-of-its-kind large language model (LLM) framework to assist patient-to-trial matching. Given a patient note, TrialGPT predicts the patient's eligibility on a criterion-by-criterion basis and then consolidates these predictions to assess the patient's eligibility for the target trial. We evaluate the trial-level prediction performance of TrialGPT on three publicly available cohorts of 184 patients with over 18,000 trial annotations. We also engaged three physicians to label over 1,000 patient-criterion pairs to assess its criterion-level prediction accuracy. Experimental results show that TrialGPT achieves a criterion-level accuracy of 87.3% with faithful explanations, close to the expert performance (88.7%-90.0%). The aggregated TrialGPT scores are highly correlated with human eligibility judgments, and they outperform the best-competing models by 32.6% to 57.2% in ranking and excluding clinical trials. Furthermore, our user study reveals that TrialGPT can significantly reduce the screening time (by 42.6%) in a real-life clinical trial matching task. These results and analyses have demonstrated promising opportunities for clinical trial matching with LLMs such as TrialGPT. |
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| تواريخ الأحداث: | Date Created: 20230814 Latest Revision: 20240509 |
| رمز التحديث: | 20240509 |
| مُعرف محوري في PubMed: | PMC10418514 |
| PMID: | 37576126 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2331-8422 |
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