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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generation.

Jimyung Park¹, Yilu Fang¹, Casey Ta¹

¹Department of Biomedical Informatics, Columbia University, New York, United States.

Journal of Biomedical Informatics

|May 2, 2024

Summary

This summary is machine-generated.

Criteria2Query (C2Q) 3.0 uses GPT-4 to convert clinical trial eligibility criteria into database queries, improving patient identification accuracy. Further research is needed to ensure large language model reliability in clinical research.

Keywords:

Artificial intelligence ChatGPT Eligibility prescreening Human–computer collaboration Large language models

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Area of Science:

Clinical Informatics
Artificial Intelligence in Medicine
Health Data Science

Background:

Automated patient identification for clinical research is a significant challenge.
Existing methods for translating eligibility criteria into database queries are often manual and time-consuming.
The Criteria2Query (C2Q) system aims to streamline this process.

Purpose of the Study:

To introduce and evaluate Criteria2Query (C2Q) 3.0, a system utilizing GPT-4 for semi-automatic conversion of clinical trial eligibility criteria into executable database queries.
To assess the performance of GPT-4 in concept extraction and SQL query generation for clinical trial eligibility criteria.

Main Methods:

C2Q 3.0 employed three GPT-4 prompts for concept extraction, SQL query generation, and reasoning.
Concept extraction was benchmarked against manual annotations from 20 clinical trials.
SQL generation accuracy and reasoning quality were evaluated by domain experts on subsets of clinical trials.

Main Results:

GPT-4 achieved an F1-score of 0.891 for concept extraction from 518 concepts.
The system identified 29 errors in SQL generation, with logic errors being most frequent (34.48%).
Reasoning evaluation showed high coherence (mean 4.70) and usefulness (mean 4.37), but lower readability (mean 3.95).

Conclusions:

GPT-4 significantly enhances the accuracy of extracting clinical trial eligibility criteria concepts within the C2Q 3.0 system.
The findings highlight the potential of large language models in clinical research data management.
Continued investigation is necessary to fully establish the reliability of large language models for clinical applications.