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Large Language Models for Automating Clinical Trial Criteria Conversion to Observational Medical Outcomes Partnership

Kye Hwa Lee1, Sujung Jang2, Grace Juyun Kim3

  • 1Department of Information Medicine, Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, 82 10-3010-5991, 82 2-3010-2531.

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Summary
This summary is machine-generated.

Automating clinical trial eligibility criteria conversion to SQL queries is challenging. Smaller models like llama3:8b show promise by outperforming larger models with lower hallucination rates, though validation remains crucial.

Keywords:
OMOP CDMObservational Medical Outcomes Partnership Common Data ModelSQL generationclinical trialseligibility criteriafeasibility assessmenthallucinationlarge language modelsnatural language processing

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Trial Design

Background:

  • Real-world data enhances clinical trial design.
  • Automating eligibility criteria conversion to database queries faces accuracy and usability challenges.

Purpose of the Study:

  • Develop an automated system to convert free-text eligibility criteria from ClinicalTrials.gov into OMOP CDM-compatible SQL queries.
  • Evaluate hallucination patterns across multiple large language models (LLMs) to identify optimal deployment strategies.

Main Methods:

  • Implemented a three-stage preprocessing pipeline (segmentation, filtering, simplification) for clinical semantics preservation.
  • Compared GPT-4 and USAGI for concept mapping accuracy using 357 clinical terms.
  • Analyzed 760 SQL generation attempts across 8 LLMs and 5 prompting strategies using SynPUF data.
  • Validated generated SQL queries against established concept sets using an OMOP CDM database.

Main Results:

  • GPT-4 achieved 48.5% concept mapping accuracy, outperforming USAGI (32.0%).
  • The open-source llama3:8b model demonstrated the highest effective SQL rate (75.8%), surpassing GPT-4 (45.3%) due to lower hallucination rates (21.1%).
  • Overall hallucination rate was 32.7%, with common errors including wrong domain assignments and placeholder insertions.
  • Clinical validation showed variable performance, with high concordance for Type 1 Diabetes (Jaccard=0.81) but minimal for Type 2 Diabetes (Jaccard=0.03).

Conclusions:

  • LLMs can expedite eligibility criteria transformation, but hallucination rates necessitate careful model selection and validation.
  • Smaller, cost-effective models like llama3:8b can outperform larger commercial LLMs.
  • Future research should explore hybrid approaches combining LLMs with rule-based methods for complex clinical concepts.