End-To-End Clinical Trial Matching with Large Language Models

• URL: http://arxiv.org/abs/2407.13463v1
• Date: Thu, 18 Jul 2024 12:36:26 GMT
• Title: End-To-End Clinical Trial Matching with Large Language Models
• Authors: Dyke Ferber, Lars Hilgers, Isabella C. Wiest, Marie-Elisabeth Leßmann, Jan Clusmann, Peter Neidlinger, Jiefu Zhu, Georg Wölflein, Jacqueline Lammert, Maximilian Tschochohei, Heiko Böhme, Dirk Jäger, Mihaela Aldea, Daniel Truhn, Christiane Höper, Jakob Nikolas Kather,
• Abstract summary: 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.
• Score: 0.6151041580858937
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Matching cancer patients to clinical trials is essential for advancing treatment and patient care. However, the inconsistent format of medical free text documents and complex trial eligibility criteria make this process extremely challenging and time-consuming for physicians. We investigated whether the entire trial matching process - from identifying relevant trials among 105,600 oncology-related clinical trials on clinicaltrials.gov to generating criterion-level eligibility matches - could be automated using Large Language Models (LLMs). Using GPT-4o and a set of 51 synthetic Electronic Health Records (EHRs), we demonstrate that our approach identifies relevant candidate trials in 93.3% of cases and achieves a preliminary accuracy of 88.0% when matching patient-level information at the criterion level against a baseline defined by human experts. Utilizing LLM feedback reveals that 39.3% criteria that were initially considered incorrect are either ambiguous or inaccurately annotated, leading to a total model accuracy of 92.7% after refining our human baseline. In summary, we present an end-to-end pipeline for clinical trial matching using LLMs, demonstrating high precision in screening and matching trials to individual patients, even outperforming the performance of qualified medical doctors. Our fully end-to-end pipeline can operate autonomously or with human supervision and is not restricted to oncology, offering a scalable solution for enhancing patient-trial matching in real-world settings.

Related papers

• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• 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)
• Zero-Shot Clinical Trial Patient Matching with LLMs [40.31971412825736]
  Large language models (LLMs) offer a promising solution to automated screening. We design an LLM-based system which, given a patient's medical history as unstructured clinical text, evaluates whether that patient meets a set of inclusion criteria. Our system achieves state-of-the-art scores on the n2c2 2018 cohort selection benchmark.
  arXiv  Detail & Related papers  (2024-02-05T00:06:08Z)
• Matching Patients to Clinical Trials with Large Language Models [29.265158319106604]
  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)
  arXiv  Detail & Related papers  (2023-07-27T17:56:56Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• 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)
• 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)
• Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching [49.78442796596806]
  We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM) Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
  arXiv  Detail & Related papers  (2023-03-24T03:14:00Z)
• 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)

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.