Matching Patients to Clinical Trials with Large Language Models

• URL: http://arxiv.org/abs/2307.15051v5
• Date: Mon, 18 Nov 2024 03:55:02 GMT
• Title: Matching Patients to Clinical Trials with Large Language Models
• Authors: Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen, Changlin Gong, Dara Bracken-Clarke, Elisabetta Xue, Yifan Yang, Jimeng Sun, Zhiyong Lu,
• Abstract summary: 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)
• Score: 29.265158319106604
• License:
• Abstract: 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 1,015 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.

Related papers

• End-To-End Clinical Trial Matching with Large Language Models [0.6151041580858937]
  We present an end-to-end pipeline for clinical trial matching using Large Language Models (LLMs) Our approach identifies relevant candidate trials in 93.3% of cases and achieves a preliminary accuracy of 88.0%. Our fully end-to-end pipeline can operate autonomously or with human supervision and is not restricted to oncology.
  arXiv  Detail & Related papers  (2024-07-18T12:36:26Z)
• Accelerating Clinical Evidence Synthesis with Large Language Models [28.002870749019035]
  We introduce TrialMind, a generative artificial intelligence pipeline for facilitating human-AI collaboration. TrialMind excels across study search, screening, and data extraction tasks. Human experts favored TrialMind's outputs over GPT-4's in 62.5% to 100% of cases.
  arXiv  Detail & Related papers  (2024-06-25T17:41:52Z)
• PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models [4.438101430231511]
  We present the first, end-to-end large-scale empirical evaluation of clinical trial matching using real-world EHRs. Our study showcases the capability of LLMs to accurately match patients with appropriate clinical trials.
  arXiv  Detail & Related papers  (2024-04-23T22:33:19Z)
• AutoTrial: Prompting Language Models for Clinical Trial Design [53.630479619856516]
  We present a method named AutoTrial to aid the design of clinical eligibility criteria using language models. Experiments on over 70K clinical trials verify that AutoTrial generates high-quality criteria texts.
  arXiv  Detail & Related papers  (2023-05-19T01:04:16Z)
• Improving Patient Pre-screening for Clinical Trials: Assisting Physicians with Large Language Models [0.0]
  Large Language Models (LLMs) have shown to perform well for clinical information extraction and clinical reasoning. This paper investigates the use of InstructGPT to assist physicians in determining eligibility for clinical trials based on a patient's summarised medical profile.
  arXiv  Detail & Related papers  (2023-04-14T21:19:46Z)
• SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning [67.8195828626489]
  Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. We propose Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first identifies trial topics to cluster the multi-sourced trial data into relevant trial topics. With the consideration of each trial sequence as a task, it uses a meta-learning strategy to achieve a point where the model can rapidly adapt to new tasks with minimal updates.
  arXiv  Detail & Related papers  (2023-04-07T23:04:27Z)
• Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint [50.35075018041199]
  This work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased.
  arXiv  Detail & Related papers  (2023-03-24T03:59:19Z)
• COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching [70.08786840301435]
  We propose CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching.
  arXiv  Detail & Related papers  (2020-06-15T21:01:33Z)
• Comparative Analysis of Predictive Methods for Early Assessment of Compliance with Continuous Positive Airway Pressure Therapy [55.41644538483948]
  compliance with continuous positive airway pressure (CPAP) is accepted as more than 4h of CPAP average use nightly. Previous works already reported factors significantly related to compliance with the therapy. This work intends to take a further step in this direction by building compliance classifiers with CPAP therapy at three different moments of the patient follow-up.
  arXiv  Detail & Related papers  (2019-12-27T14:44:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.