Matching patients to clinical trials with large language models(nature.com)

Matching patients to clinical trials with large language models

https://www.nature.com/articles/s41467-024-53081-z

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"Matching Patients to Clinical Trials with Large Language Models" (2024) https://arxiv.org/abs/2307.15051

Src: https://github.com/ncbi-nlp/TrialGPT

westurner2 lata temu

NewsArticle: "NIH-developed AI algorithm matches potential volunteers to clinical trials" (2024) https://www.nih.gov/news-events/news-releases/nih-developed-... :

> Such an algorithm may save clinicians time and accelerate clinical enrollment and research [...]

> A study published in Nature Communications found that the AI algorithm, called TrialGPT, could successfully identify relevant clinical trials for which a person is eligible and provide a summary that clearly explains how that person meets the criteria for study enrollment. The researchers concluded that this tool could help clinicians navigate the vast and ever-changing range of clinical trials available to their patients, which may lead to improved clinical trial enrollment and faster progress in medical research.

jadynqa2 lata temu

Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models. TrialGPT comprises three modules: it first performs large-scale filtering to retrieve candidate trials (TrialGPT-Retrieval); then predicts criterion-level patient eligibility (TrialGPT-Matching); and finally generates trial-level scores (TrialGPT-Ranking). We evaluate TrialGPT on three cohorts of 183 synthetic patients with over 75,000 trial annotations. TrialGPT-Retrieval can recall over 90% of relevant trials using less than 6% of the initial collection. Manual evaluations on 1015 patient-criterion pairs show that TrialGPT-Matching achieves an accuracy of 87.3% with faithful explanations, close to the expert performance. The TrialGPT-Ranking scores are highly correlated with human judgments and outperform the best-competing models by 43.8% in ranking and excluding trials. Furthermore, our user study reveals that TrialGPT can reduce the screening time by 42.6% in patient recruitment. Overall, these results have demonstrated promising opportunities for patient-to-trial matching with TrialGPT.