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Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2016 (English)In: Proceedings of the 15th Workshop on Biomedical Natural Language Processing, Association for Computational Linguistics, 2016, 191-195 p.Conference paper (Refereed)
Abstract [en]

Detecting healthcare-associated infections pose a major challenge in healthcare. Using natural language processing and machine learning applied on electronic patient records is one approach that has been shown to work. However the results indicate that there was room for improvement and therefore we have applied deep learning methods. Specifically we implemented a network of stacked sparse auto encoders and a network of stacked restricted Boltzmann machines. Our best results were obtained using the stacked restricted Boltzmann machines with a precision of 0.79 and a recall of 0.88.

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2016. 191-195 p.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-136578ISBN: 978-1-945626-12-8 (print)OAI: oai:DiVA.org:su-136578DiVA: diva2:1055442
Conference
15th Workshop on Biomedical Natural Language Processing, Berlin, Germany, August 12, 2016
Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2017-02-13Bibliographically approved

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CiteExportLink to record
Permanent link

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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
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  • asciidoc
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