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LeafAI: query generator for clinical cohort discovery rivaling a human programmer.

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Summary

LeafAI, a novel system, enhances clinical trial recruitment by automatically generating data model-agnostic queries. It significantly outperforms human programmers in identifying eligible patients, saving valuable research time.

Keywords:
clinical trialscohort definitionelectronic health recordsmachine learningnatural language processing

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Biomedical Data Science

Background:

  • Accurate patient identification from clinical databases is crucial for clinical research.
  • Designing effective queries for complex eligibility criteria demands specialized expertise.
  • Existing methods often lack data model agnosticism and advanced reasoning capabilities.

Purpose of the Study:

  • To develop a system for generating data model-agnostic queries for clinical trial eligibility criteria.
  • To incorporate novel logical reasoning for complex criteria.
  • To automate and improve the efficiency of patient identification in clinical research.

Main Methods:

  • Utilized hybrid deep learning and rule-based modules for text processing tasks.
  • Integrated the Unified Medical Language System (UMLS) and linked ontologies.
  • Developed a novel method for tagging database schema elements with UMLS concepts.
  • Evaluated the system (LeafAI) against human database programmers using real clinical trial data.

Main Results:

  • LeafAI matched a mean of 43% of enrolled patients across 8 trials, compared to 27% matched by a human programmer.
  • LeafAI identified 27,225 eligible patients, significantly more than the 14,587 identified by the human programmer.
  • Query generation time was reduced from hours for humans to minutes for LeafAI.

Conclusions:

  • LeafAI represents a state-of-the-art system for data model-agnostic query generation with conditional reasoning.
  • The system demonstrates the capability to match or exceed the performance of experienced human programmers in patient cohort identification.
  • This technology has the potential to accelerate clinical trial recruitment and streamline research processes.