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Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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Cue-based assertion classification for Swedish clinical text--developing a lexicon for pyConTextSwe.

Sumithra Velupillai1, Maria Skeppstedt1, Maria Kvist2

  • 1Department of Computer and Systems Sciences (DSV), Stockholm University, Forum 100, 164 40 Kista, Sweden.

Artificial Intelligence in Medicine
|February 22, 2014
PubMed
Summary
This summary is machine-generated.

We developed an optimized assertion lexicon for clinical Swedish, significantly improving the pyConTextSwe system's accuracy in identifying affirmed, negated, or uncertain disorders. This enhanced Swedish clinical text assertion system is now publicly available.

Keywords:
Assertion classificationClinical text miningDictionariesElectronic health recordsInformation extractionMedical Language Processing

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Area of Science:

  • Natural Language Processing
  • Clinical Informatics
  • Computational Linguistics

Background:

  • Assertion systems rely on cue lexicons for accurate disorder identification.
  • Porting assertion systems across languages requires language-specific lexicon optimization.
  • Previous work established the pyConTextNLP system for English clinical text.

Purpose of the Study:

  • To create an optimized assertion lexicon for clinical Swedish.
  • To port the pyConTextNLP assertion system to Swedish (pyConTextSwe).
  • To evaluate the performance of pyConTextSwe with the optimized lexicon.

Main Methods:

  • Integrated cues from four external lexicons and generated inflections/combinations.
  • Applied four assertion classes and two binary classes to pyConTextSwe on Swedish clinical corpus subsets.
  • Compared system performance with and without added cues, refining the lexicon through error analysis.

Main Results:

  • Added 454 cues, resulting in statistically significant improvements (83% F-score overall on development set).
  • Achieved 81% overall F-score on the evaluation set.
  • Individual assertion class F-scores ranged from 55% to 88%; binary classification F-scores reached up to 97%.

Conclusions:

  • Successfully ported pyConTextNLP to Swedish as pyConTextSwe.
  • Developed an extensive and useful assertion lexicon for Swedish clinical text.
  • The optimized lexicon is a valuable resource and publicly available.