AutoTrial: Prompting Language Models for Clinical Trial Design

• URL: http://arxiv.org/abs/2305.11366v2
• Date: Sun, 8 Oct 2023 03:44:35 GMT
• Title: AutoTrial: Prompting Language Models for Clinical Trial Design
• Authors: Zifeng Wang and Cao Xiao and Jimeng Sun
• Abstract summary: 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.
• Score: 53.630479619856516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical trials are critical for drug development. Constructing the appropriate eligibility criteria (i.e., the inclusion/exclusion criteria for patient recruitment) is essential for the trial's success. Proper design of clinical trial protocols should consider similar precedent trials and their eligibility criteria to ensure sufficient patient coverage. In this paper, we present a method named AutoTrial to aid the design of clinical eligibility criteria using language models. It allows (1) controllable generation under instructions via a hybrid of discrete and neural prompting, (2) scalable knowledge incorporation via in-context learning, and (3) explicit reasoning chains to provide rationales for understanding the outputs. Experiments on over 70K clinical trials verify that AutoTrial generates high-quality criteria texts that are fluent and coherent and with high accuracy in capturing the relevant clinical concepts to the target trial. It is noteworthy that our method, with a much smaller parameter size, gains around 60% winning rate against the GPT-3.5 baselines via human evaluations.

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)
• Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model [0.7373617024876725]
  Clinical trials are essential for advancing pharmaceutical interventions, but they face a bottleneck in selecting eligible participants. The complex nature of unstructured medical texts presents challenges in efficiently identifying participants. In this study, we aimed to evaluate the performance of a prompt-based large language model for the cohort selection task.
  arXiv  Detail & Related papers  (2024-04-24T20:42:28Z)
• 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)
• Text Classification of Cancer Clinical Trial Eligibility Criteria [3.372747046563984]
  We focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. Our results demonstrate the feasibility of automatically classifying common exclusion criteria.
  arXiv  Detail & Related papers  (2023-09-14T15:59:16Z)
• 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)
• 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)
• The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria [1.7205106391379026]
  We introduce the Leaf Clinical Trials (LCT) corpus, a human-annotated corpus of over 1,000 clinical trial eligibility criteria descriptions. We provide details of our schema, annotation process, corpus quality, and statistics.
  arXiv  Detail & Related papers  (2022-07-27T19:22:24Z)
• 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)
• Contextual Constrained Learning for Dose-Finding Clinical Trials [102.8283665750281]
  C3T-Budget is a contextual constrained clinical trial algorithm for dose-finding under both budget and safety constraints. It recruits patients with consideration of the remaining budget, the remaining time, and the characteristics of each group.
  arXiv  Detail & Related papers  (2020-01-08T11:46:48Z)

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.