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<h1>TrialGPT FAQ</h1>
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<h2>What is TrialGPT?</h2>
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<div class="publication faq">
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||
TrialGPT is an end-to-end framework designed for patient-to-trial matching, leveraging large language models (LLMs).
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||
It consists of three main modules: TrialGPT-Retrieval for filtering and retrieving relevant clinical trials,
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||
TrialGPT-Matching for determining patient eligibility against trial criteria with truthful explanations,
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||
and TrialGPT-Ranking for scoring and ranking the trials based on patient suitability. Given a patient summary,
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||
TrialGPT can return a list of clinical trial recommendations from a large collection of candidates,
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||
based on the patient’s eligibility.
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</div>
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<h2>How does TrialGPT work?</h2>
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<div class="publication faq">
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||
TrialGPT operates through three sequential components:
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||
<ul>
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||
<li>
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||
<strong>TrialGPT-Retrieval:</strong> This module uses LLMs to generate keywords from patient summaries to
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||
filter out the most irrelevant clinical trials from an initial, large pool using a combination of lexical and
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semantic retrieval methods.
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||
</li>
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||
<li>
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<strong>TrialGPT-Matching:</strong> Analyzes patient-trial pairs on a criterion-by-criterion basis,
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providing explanations of patient eligibility and locating relevant sentences in patient notes for each criterion.
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</li>
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||
<li>
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<strong>TrialGPT-Ranking:</strong> Aggregates criterion-level predictions to compute trial-level scores,
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||
which are used to rank the clinical trials by the extent of patient eligibility.
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</li>
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||
</ul>
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||
</div>
|
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|
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<h2>Who are targeted users of TrialGPT?</h2>
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<div class="publication faq">
|
||
The targeted users of TrialGPT include clinical trial recruiters, research teams involved in trial matching,
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||
and healthcare professionals who facilitate patient recruitment. It is designed to assist domain experts by
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||
enhancing efficiency and reducing time spent on patient-trial matching.
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||
</div>
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||
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||
<h2>Is TrialGPT also for patients?</h2>
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||
<div class="publication faq">
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||
While TrialGPT can empower individual patients to identify potential clinical trials they may qualify for,
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||
its primary design focuses on supporting clinical and research staff to facilitate patient matching.
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||
Patients might access its benefits through healthcare intermediaries at this point.
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||
</div>
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||
|
||
<h2>Is the TrialGPT software freely available?</h2>
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<div class="publication faq">
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||
Yes, the TrialGPT framework is publicly accessible. The code and related resources can be found on
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||
its GitHub repository:
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||
<a href="https://github.com/ncbi-nlp/TrialGPT" target="_blank">https://github.com/ncbi-nlp/TrialGPT</a>.
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||
</div>
|
||
|
||
<h2>Do you have a web-based system for TrialGPT?</h2>
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||
<div class="publication faq">
|
||
No. Although one can potentially use the official code to deploy a web-based system for TrialGPT,
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||
we do not provide an official implementation.
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||
</div>
|
||
|
||
<h2>Who built TrialGPT?</h2>
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<div class="publication faq">
|
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TrialGPT is developed by researchers from the Division of Intramural Research,
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||
National Library of Medicine, National Institutes of Health.
|
||
</div>
|
||
|
||
<h2>How is TrialGPT different from other similar systems or tools?</h2>
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||
<div class="publication faq">
|
||
Unlike traditional embedding-based methods such as DeepEnroll <a href="#ref1">[1]</a>
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||
and COMPOSE <a href="#ref2">[2]</a> which require massive training data and are not explainable, TrialGPT can perform
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||
zero-shot and explainable patient-to-trial matching without requiring any training data.
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||
Unlike structuring-based methods such as Criteria2Query <a href="#ref3">[3]</a>, TrialGPT does not depend on
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||
the ontology mapping of clinical trial criteria, which is a non-trivial task itself.
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||
|
||
<br><br>
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||
<strong>References</strong>
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||
<ol>
|
||
<li id="ref1">
|
||
Zhang X, Xiao C, Glass LM, et al. DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction.
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||
<a href="https://doi.org/10.1145/3366423.3380181" target="_blank">
|
||
Proceedings of The Web Conference 2020.
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||
New York, NY, USA: Association for Computing Machinery 2020:1029–37
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||
</a>
|
||
</li>
|
||
<li id="ref2">
|
||
Gao J, Xiao C, Glass LM, et al. COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching.
|
||
<a href="https://dl.acm.org/doi/10.1145/3394486.3403123" target="_blank">
|
||
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
|
||
New York, NY, USA: Association for Computing Machinery 2020:803–12
|
||
</a>
|
||
</li>
|
||
<li id="ref3">
|
||
Fang Y, Idnay B, Sun Y, et al. Combining human and machine intelligence for clinical trial eligibility querying.
|
||
<a href="https://doi.org/10.1093/jamia/ocac051" target="_blank">
|
||
Journal of the American Medical Informatics Association. 2022;29:1161–71. doi: 10.1093/jamia/ocac051
|
||
</a>
|
||
</li>
|
||
</ol>
|
||
</div>
|
||
|
||
<h2>How to cite TrialGPT?</h2>
|
||
<div class="publication faq">
|
||
Please cite TrialGPT as: <br><br>
|
||
Qiao Jin, Zifeng Wang, Charalampos S. Floudas, Fangyuan Chen,
|
||
Changlin Gong, Dara Bracken-Clarke, Elisabetta Xue, Yifan Yang, Jimeng Sun, Zhiyong Lu.
|
||
Matching Patients to Clinical Trials with Large Language Models.
|
||
<a href="https://www.nature.com/articles/s41467-024-53081-z" target="_blank" rel="noreferrer">
|
||
Nat Commun. 2024;15:9074. doi: 10.1038/s41467-024-53081-z
|
||
</a><br><br>
|
||
</div>
|
||
|
||
<h2>I heard you are working on TrialGPT 2.0. What are your goals for that?</h2>
|
||
<div class="publication faq">
|
||
Given the promising benchmarking results, the research team was recently selected for The
|
||
<a href="https://oir.nih.gov/sourcebook/awards-fellowships-grant-opportunities/directors-challenge-innovation-award-program/2024-directors-challenge-awards#2024-directors-lu" target="_blank">
|
||
Director’s Challenge Innovation Award
|
||
</a>
|
||
to further assess the model’s performance and effectiveness in real-world clinical settings.
|
||
The researchers anticipate that this work could make clinical trial recruitment more effective
|
||
and help reduce barriers to participation for patients seeking to join clinical research.
|
||
</div>
|
||
|
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</div>
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