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dc.contributor.authorYogarajan, Vithyaen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorMayo, Michaelen_NZ
dc.date.accessioned2019-10-29T22:01:46Z
dc.date.available2019en_NZ
dc.date.available2019-10-29T22:01:46Z
dc.date.issued2019en_NZ
dc.identifier.citationYogarajan, V., Pfahringer, B., & Mayo, M. (2019). Automatic end-to-end De-identification: Is high accuracy the only metric? arXiv.en
dc.identifier.urihttps://hdl.handle.net/10289/13054
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 de-identification. 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.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.urihttp://arxiv.org/pdf/1901.10583v1en_NZ
dc.subjectautomatic de-identificationen_NZ
dc.subjectpatient privacyen_NZ
dc.subjectelectronic health recordsen_NZ
dc.subjectfree texten_NZ
dc.subjectaccuracy
dc.subjectchallenges
dc.subjectrisks
dc.subjectsurrogate generation
dc.subjectre-identification
dc.subjectusability
dc.subjectreview
dc.subjectMachine learning
dc.subjectMachine learning
dc.titleAutomatic end-to-end De-identification: Is high accuracy the only metric?en_NZ
dc.typeJournal Article
dc.relation.isPartOfarXiven_NZ
pubs.declined2019-02-06T13:19:41.791+1300
pubs.deleted2019-02-06T13:19:41.791+1300
pubs.elements-id234008


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