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dc.contributor.authorDehghan, Azad
dc.contributor.authorKovacevic, A
dc.contributor.authorKarystianis, G
dc.contributor.authorKeane, J
dc.contributor.authorNenadic, G
dc.date.accessioned2017-07-13T10:44:18Z
dc.date.available2017-07-13T10:44:18Z
dc.date.issued2017-06-07
dc.identifier.citationLearning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes. 2017 J Biomed Informen
dc.identifier.issn1532-0480
dc.identifier.pmid28602908
dc.identifier.doi10.1016/j.jbi.2017.06.005
dc.identifier.urihttp://hdl.handle.net/10541/620440
dc.description.abstractDe-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial. The results show that the integration of the proposed methods can identify Health Information Portability and Accountability Act (HIPAA) defined PHIs with overall F1-scores of ∼90% and above. Yet, some classes (Profession, Organization) proved again to be challenging given the variability of expressions used to reference given information.
dc.language.isoenen
dc.rightsArchived with thanks to Journal of biomedical informaticsen
dc.titleLearning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.en
dc.typeArticleen
dc.contributor.departmentSchool of Computer Science, University of Manchester, Manchester, UKen
dc.identifier.journalJournal of Biomedical Informaticsen
html.description.abstractDe-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial. The results show that the integration of the proposed methods can identify Health Information Portability and Accountability Act (HIPAA) defined PHIs with overall F1-scores of ∼90% and above. Yet, some classes (Profession, Organization) proved again to be challenging given the variability of expressions used to reference given information.


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