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Automated Diagnosis Coding with Combined Text Representations
University of Vienna, Computer Science, .
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2017 (English)In: Studies in Health Technology and Informatics, Vol. 235Article in journal (Refereed)
Abstract [en]

Automated diagnosis coding can be provided efficiently by learning predictive models from historical data; however, discriminating between thousands of codes while allowing a variable number of codes to be assigned is extremely difficult. Here, we explore various text representations and classification models for assigning ICD-9 codes to discharge summaries in MIMIC-III. It is shown that the relative effectiveness of the investigated representations depends on the frequency of the diagnosis code under consideration and that the best performance is obtained by combining models built using different representations.

Place, publisher, year, edition, pages
IOS Press , 2017. Vol. 235
Keyword [en]
Electronic health records, diagnosis coding, predictive modeling
National Category
Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-144896DOI: 10.3233/978-1-61499-753-5-201OAI: oai:DiVA.org:su-144896DiVA: diva2:1117543
Available from: 2017-06-29 Created: 2017-06-29

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Publisher's full texthttp://ebooks.iospress.nl/publication/46330
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Language Technology (Computational Linguistics)

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf