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Related Experiment Videos

A corpus driven approach applying the "frame semantic" method for modeling functional status terminology.

Alexander P Ruggieri1, Serguei V Pakhomov, Christopher G Chute

  • 1Division of Medical Informatics Research, Department of Health Sciences Research, Harwick 826, Mayo Clinic, 200 SW First Street, Rochester, MN 55905, USA. Ruggieri@mayo.edu

Studies in Health Technology and Informatics
|September 14, 2004
PubMed
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This study introduces the frame semantic method for developing functional status terminology. This approach aids in creating semantic models for better machine recognition of clinical data.

Area of Science:

  • Computational Linguistics
  • Medical Informatics
  • Clinical Terminology

Background:

  • Developing standardized functional status terminology is crucial for clinical practice and research.
  • Existing methods may not fully capture the semantic nuances of functional status data.
  • The FrameNet Project provides a linguistic framework for semantic analysis.

Purpose of the Study:

  • To explore the utility of the frame semantic method for functional status terminology development.
  • To create generalizable semantic models from clinical assessment instruments.
  • To assess the potential for machine recognition of functional status data.

Main Methods:

  • Applied the frame semantic method, based on linguistic theory of thematic roles.
  • Derived descriptive sentences from functional status assessment questionnaires.

Related Experiment Videos

  • Manually annotated syntactic constituents and tagged them as frame elements based on semantic roles.

    • Main Results:

    • Identified generalizable semantic frames with recurring frame elements.
    • Demonstrated the applicability of the frame semantic method to functional status data.
    • Established a foundation for machine recognition of clinical functional status information.

    Conclusions:

    • The frame semantic method offers a robust approach to developing functional status terminology.
    • This methodology can enhance the semantic modeling of clinical data.
    • It holds promise for improving automated analysis of functional status in clinical narratives.