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dc.contributor.authorYogarajan, Vithyaen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.date.accessioned2020-05-28T23:58:48Z
dc.date.available2020-02-06en_NZ
dc.date.available2020-05-28T23:58:48Z
dc.date.issued2020en_NZ
dc.identifier.citationYogarajan, V., Pfahringer, B., & Mayo, M. (2020). A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric? Applied Artificial Intelligence. https://doi.org/10.1080/08839514.2020.1718343en
dc.identifier.issn0883-9514en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/13594
dc.description.abstractDe-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic deidentification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.
dc.language.isoenen_NZ
dc.publisherTaylor & Francis Incen_NZ
dc.rightsThis is an author’s accepted version of an article published in the journal: Journal of Applied Artificial Intelligence. © 2020 Taylor & Francis.
dc.subjectScience & Technologyen_NZ
dc.subjectTechnologyen_NZ
dc.subjectComputer Science, Artificial Intelligenceen_NZ
dc.subjectEngineering, Electrical & Electronicen_NZ
dc.subjectComputer Scienceen_NZ
dc.subjectEngineeringen_NZ
dc.subjectPRIVACYen_NZ
dc.subjectMODELen_NZ
dc.subjectMachine learning
dc.titleA review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric?en_NZ
dc.typeJournal Article
dc.identifier.doi10.1080/08839514.2020.1718343en_NZ
dc.relation.isPartOfApplied Artificial Intelligenceen_NZ
pubs.elements-id251109
pubs.publication-statusPublisheden_NZ
dc.identifier.eissn1087-6545en_NZ


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