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Detecting Protected Health Information in Heterogeneous Clinical Notes
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 1879-8365Article in journal (Refereed) Published
Abstract [en]

To enable secondary use of healthcare data in a privacy- preserving manner, there is a need for methods capable of automatically identifying protected health information (PHI) in clinical text. To that end, learning predictive models from labeled examples has emerged as a promising alternative to rule-based systems. However, little is known about differences with respect to PHI prevalence in different types of clinical notes and how potential domain differences may affect the performance of predictive models trained on one particular type of note and applied to another. In this study, we analyze the performance of a predictive model trained on an existing PHI corpus of Swedish clinical notes and applied to a variety of clinical notes: written (i) in different clinical specialities, (ii) under different headings, and (iii) by persons in different professions. The results indicate that domain adaption is needed for effective detection of PHI in heterogeneous clinical notes.

Place, publisher, year, edition, pages
2017.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-150179OAI: oai:DiVA.org:su-150179DiVA: diva2:1165765
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2018-01-13

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Henriksson, AronKvist, MariaDalianis, Hercules
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