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Automated Diagnosis Coding with Combined Text Representations
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2017 (English)In: Informatics for Health: Connected Citizen-Led Wellness and Population Health / [ed] Rebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J. Scott, IOS Press, 2017, p. 201-205Conference paper, Published paper (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. p. 201-205
Series
Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365 ; 235
Keywords [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: 10.3233/978-1-61499-753-5-201ISBN: 978-1-61499-752-8 (print)ISBN: 978-1-61499-753-5 (electronic)OAI: oai:DiVA.org:su-144896DiVA, id: diva2:1117543
Conference
The Medical Informatics Europe (MIE) Conference, Manchester, UK, 24-26 April, 2017
Available from: 2017-06-29 Created: 2017-06-29 Last updated: 2022-02-28Bibliographically approved

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Henriksson, Aron

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • 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