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Related Experiment Video

Updated: Jul 5, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Syntactical negation detection in clinical practice guidelines.

Stefan Gindl1, Katharina Kaiser, Silvia Miksch

  • 1Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria. stefan.gindl@gmx.at

Studies in Health Technology and Informatics
|May 20, 2008
PubMed
Summary

Detecting negation in clinical practice guidelines (CPGs) is crucial. Our developed algorithms, utilizing syntactical methods, significantly improve negation detection in medical texts.

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09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

  • Medical Informatics
  • Natural Language Processing

Background:

  • Clinical practice guidelines (CPGs) contain vital medical information presented in narrative text.
  • A significant portion of information within CPGs is expressed in a negated form, indicating absence or contraindication.

Purpose of the Study:

  • To develop and evaluate algorithms for effective negation detection in clinical practice guidelines.
  • To highlight the importance of identifying negated information for clinical decision-making.

Main Methods:

  • Development of specialized algorithms for Negation Detection.
  • Application of syntactical methods to analyze medical documents.

Main Results:

  • The developed algorithms demonstrate capability in detecting negation within CPGs.
  • Syntactical methods were found to enhance the accuracy of negation detection.

Conclusions:

  • Negation Detection in CPGs is an important task for identifying non-occurring conditions and contraindicated treatments.
  • Syntactical approaches offer a promising method for improving negation detection in both medical and general narrative texts.