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Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2017 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 235, p. 241-245Article in journal (Refereed) Published
Abstract [en]

The mapping of unstructured clinical text to an ontology facilitates meaningful secondary use of health records but is non-trivial due to lexical variation and the abundance of misspellings in hurriedly produced notes. Here, we apply several spelling correction methods to Swedish medical text and evaluate their impact on SNOMED CT mapping; first in a controlled evaluation using medical literature text with induced errors, followed by a partial evaluation on clinical notes. It is shown that the best-performing method is context-sensitive, taking into account trigram frequencies and utilizing a corpus-based dictionary.

Place, publisher, year, edition, pages
2017. Vol. 235, p. 241-245
Keywords [en]
spelling correction, terminology mapping, clinical text
National Category
Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-149424DOI: 10.3233/978-1-61499-753-5-241OAI: oai:DiVA.org:su-149424DiVA, id: diva2:1161592
Note

ISBN: 978-1-61499-752-8 (print), 978-1-61499-753-5 (online).

Available from: 2017-11-30 Created: 2017-11-30 Last updated: 2018-01-13Bibliographically approved

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Henriksson, AronDuneld, Martin
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  • apa
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  • de-DE
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  • nn-NB
  • sv-SE
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Output format
  • html
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  • asciidoc
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