SCAN: A Swedish Clinical Abbreviation Normalizer

Further Development and Adaptation to Radiology
  • Maria Kvist
  • Sumithra Velupillai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8685)


Abbreviations pose a challenge for information extraction systems. In clinical text, abbreviations are abundant, as this type of documentation is written under time-pressure. We report work on characterizing abbreviations in Swedish clinical text and the development of SCAN: a Swedish Clinical Abbreviation Normalizer, which is built for the purpose of improving information access systems in the clinical domain. The clinical domain includes several subdomains with differing vocabularies depending on the nature of the specialist work, and adaption of NLP-tools may consequently be necessary. We extend and adapt SCAN, and evaluate on two different clinical subdomains: emergency department (ED) and radiology (X-ray). Overall final results are 85% (ED) and 83% (X-ray) F1-measure on the task of abbreviation identification. We also evaluate coverage of abbreviation expansion candidates in existing lexical resources, and create two new, freely available, lexicons with abbreviations and their possible expansions for the two clinical subdomains.


Emergency Department Natural Language Processing Radiology Report Clinical Domain Lexical Resource 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Maria Kvist
    • 1
    • 2
  • Sumithra Velupillai
    • 1
  1. 1.Dept. of Computer and Systems Sciences (DSV)Stockholm UniversityKistaSweden
  2. 2.Department of Learning, Informatics, Management and Ethics (LIME)Karolinska InstitutetSweden

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