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This study introduces a novel semantic parsing method to convert natural language questions into logical forms for electronic health records (EHRs). This approach achieves high precision, enabling reliable clinical question answering systems.

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Data Science

Background:

  • Electronic Health Records (EHRs) contain vast amounts of structured data.
  • Accessing EHR data via natural language queries is challenging.
  • Existing methods lack the precision required for clinical settings.

Purpose of the Study:

  • To develop a high-precision method for converting natural language questions about EHR data into logical forms.
  • To enable the creation of a trusted, real-time clinical question answering system.
  • To improve clinician access to structured EHR data.

Main Methods:

  • A hybrid semantic parsing approach combining rule-based methods and machine learning classifiers.
  • Development of a parser capable of identifying unknown terms and their impact on logical structure.
  • Integration with a module for converting logical forms into EHR-specific queries.

Main Results:

  • Achieved an overall semantic parsing precision of 95.6% on a dataset of 212 questions.
  • The system can identify and report when unknown terms hinder full question understanding.
  • Demonstrated the feasibility of a verifiably correct question answering system.

Conclusions:

  • The proposed hybrid semantic parsing method significantly enhances the precision of converting natural language questions to logical forms for EHR data.
  • This high-precision approach facilitates the development of reliable and clinically usable question answering systems.
  • The system's ability to handle unknown terms increases trust and transparency for clinicians.