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Related Experiment Videos

Current trends with natural language processing

A M Rassinoux1, P A Michel, J Wagner

  • 1Faculty of Medicine, Geneva University Hospital, Switzerland.

Medinfo. MEDINFO
|January 1, 1995
PubMed
Summary
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This study presents a multilingual Natural Language Processing (NLP) system for medical text analysis and generation. The system supports Electronic Patient Records (EPR) and facilitates medical information retrieval and diagnosis encoding.

Area of Science:

  • Medical Informatics
  • Natural Language Processing (NLP)
  • Computational Linguistics

Background:

  • The medical field generates vast amounts of text data, necessitating advanced tools for efficient management and analysis.
  • Existing systems often fall short in fully realizing the potential of Electronic Patient Records (EPR).
  • Natural Language Processing (NLP) is a crucial dimension for EPR, alongside Image Processing and Decision Support Systems.

Purpose of the Study:

  • To demonstrate a developed NLP system for medical text analysis and generation.
  • To showcase the capabilities of a multilingual Medical Linguistic Knowledge Base (MLKB).
  • To present advancements in real-time sentence processing, knowledge representation, and query processing for medical data.

Main Methods:

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  • Development of a medical language analyzer and generator.
  • Implementation of a query processor and dictionary building tools.
  • Utilizing Sowa's conceptual graphs for knowledge representation in a multilingual MLKB (English, French, German).

    • Main Results:

    • Demonstration of real-time analysis of clinical narratives, including proximity processing and rule-based approaches.
    • Comparison of NLP results across English, French, and German, highlighting commonalities and language-specific aspects.
    • Generation of noun phrases for translation, application of NLP for ICD knowledge representation, and query processing on clinical reports.

    Conclusions:

    • The developed NLP system offers powerful tools for medical text analysis, retrieval, and generation.
    • The multilingual MLKB design supports international collaboration towards a universal medical NLP solution.
    • The system demonstrates significant progress in leveraging NLP for enhanced Electronic Patient Records and medical data utilization.