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Ensembles for clinical entity extraction
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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Number of Authors: 62018 (English)In: Revista de Procesamiento de Lenguaje Natural (SEPLN), ISSN 1135-5948, E-ISSN 1989-7553, no 60, p. 13-20Article in journal (Refereed) Published
Abstract [en]

Health records are a valuable source of clinical knowledge and Natural Language Processing techniques have previously been applied to the text in health records for a number of applications. Often, a first step in clinical text processing is clinical entity recognition; identifying, for example, drugs, disorders, and body parts in clinical text. However, most of this work has focused on records in English. Therefore, this work aims to improve clinical entity recognition for languages other than English by comparing the same methods on two different languages, specifically by employing ensemble methods. Models were created for Spanish and Swedish health records using SVM, Perceptron, and CRF and four different feature sets, including unsupervised features. Finally, the models were combined in ensembles. Weighted voting was applied according to the models individual F-scores. In conclusion, the ensembles improved the overall performance for Spanish and the precision for Swedish.

Place, publisher, year, edition, pages
2018. no 60, p. 13-20
Keywords [en]
Clinical entity recognition, ensembles, Swedish, Spanish
National Category
Languages and Literature
Identifiers
URN: urn:nbn:se:su:diva-156700DOI: 10.26342/2018-60-1ISI: 000430255200001OAI: oai:DiVA.org:su-156700DiVA, id: diva2:1211047
Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2018-05-30Bibliographically approved

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  • de-DE
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  • nn-NB
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  • Other locale
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  • asciidoc
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