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EC2Seq2Sql: Patient-trial matching with LLM agents.

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EC2Seq2Sql automatically converts clinical trial eligibility criteria into SQL queries for electronic health record (EHR) data. This framework enhances patient screening for clinical trials by bridging natural language criteria and structured EHR schemas.

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Trial Recruitment

Background:

  • Clinical trial recruitment faces challenges due to the discrepancy between natural language eligibility criteria and structured electronic health record (EHR) data.
  • Manual patient screening for trial eligibility is time-consuming and prone to errors.

Purpose of the Study:

  • To develop and evaluate EC2Seq2Sql, an automated framework for converting narrative clinical trial eligibility criteria into executable SQL queries.
  • To improve the efficiency and accuracy of patient identification for clinical trials using EHR data.

Main Methods:

  • A two-stage framework: 1) BART-based semantic parser converting text criteria to structured patterns. 2) LLM-based agent grounding patterns to database schema and generating SQL queries.
  • Evaluation on ClinicalTrials.gov dataset and a real-world hepatocellular carcinoma EHR cohort.

Main Results:

  • The BART parser achieved ROUGE_L of 0.8067 and BLEU of 0.8427.
  • The SQL generation stage reached 0.84 exact-match accuracy and 0.91 execution accuracy.
  • Queries on the real-world cohort achieved 0.88 clinical match accuracy, demonstrating effective patient retrieval.

Conclusions:

  • EC2Seq2Sql effectively automates the conversion of eligibility criteria to SQL for EHR-based patient screening.
  • The framework reduces manual screening efforts and offers a reproducible method for cohort identification.
  • Further validation is needed for large-scale, multi-center deployment.