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Prevalence Estimation of Protected Health Information in Swedish Clinical Text
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
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. 216-220Article in journal (Refereed) Published
Abstract [en]

Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner. Although automatic de-identification of clinical text using machine learning holds much promise, little is known about the relative prevalence of PHI in different types of clinical text and whether there is a need for domain adaptation when learning predictive models from one particular domain and applying it to another. In this study, we address these questions by training a predictive model and using it to estimate the prevalence of PHI in clinical text written (1) in different clinical specialties, (2) in different types of notes (i.e., under different headings), and (3) by persons in different professional roles. It is demonstrated that the overall PHI density is 1.57%; however, substantial differences exist across domains.

Place, publisher, year, edition, pages
2017. Vol. 235, p. 216-220
Keyword [en]
electronic health records, protected health information, de-identification, natural language processing, predictive modeling
National Category
Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-149433DOI: 10.3233/978-1-61499-753-5-216OAI: oai:DiVA.org:su-149433DiVA, id: diva2:1161601
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, AronKvist, MariaDalianis, Hercules
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CiteExportLink to record
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  • apa
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  • de-DE
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  • nn-NO
  • nn-NB
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Output format
  • html
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