Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text - Wikidata - awgldk - TriplyDB

Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text

Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text

http://www.wikidata.org/entity/Q38214496

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Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus.Challenges and Insights in Using HIPAA Privacy Rule for Clinical Text Annotation Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.Using Nonexperts for Annotating Pharmacokinetic Drug-Drug Interaction Mentions in Product Labeling: A Feasibility Study.A study of active learning methods for named entity recognition in clinical text De-identification of patient notes with recurrent neural networks.A dataset of 200 structured product labels annotated for adverse drug reactions.Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources.Scholarly Influence of the Conference and Labs of the Evaluation Forum eHealth Initiative: Review and Bibliometric Study of the 2012 to 2017 Outcomes

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Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text

http://www.wikidata.org/entity/Q38214496

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J.JBI.2014.05.002

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Journal of Biomedical Informatics

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Evaluating the effects of mach ...... dentification of clinical text

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Brett R South

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Danielle Mowery

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Jianwei Leng

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Stephane M Meystre

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Wendy W Chapman

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Ying Suo

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Óscar Ferrández

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P2860

Automated de-identification of free-text medical records.

BoB, a best-of-breed automated text de-identification system for VHA clinical documents

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

Concept-match medical data scrubbing. How pathology text can be used in research.

ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text.

Development and evaluation of an open source software tool for deidentification of pathology reports.

Automatic de-identification of textual documents in the electronic health record: a review of recent research.

Evaluating current automatic de-identification methods with Veteran's health administration clinical documents.

Evaluating the state-of-the-art in automatic de-identification.

Replacing personally-identifying information in medical records, the Scrub system

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162-172

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scholarly article

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10.1016/J.JBI.2014.05.002

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50

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2014-05-20T00:00:00Z

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262610335

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24859155

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5627768

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