Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus

https://doi.org/10.1016/j.jbi.2015.07.020Get rights and content
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Highlights

  • De-identification shared task for longitudinal clinical records.

  • Protected Health Information in records replaced with realistic surrogates.

  • First corpus of its kind available for distribution.

  • Used for Track 1 of the 2014 i2b2/UTHealth NLP shared task.

Abstract

The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on the de-identification of longitudinal medical records. For this track, we de-identified a set of 1304 longitudinal medical records describing 296 patients. This corpus was de-identified under a broad interpretation of the HIPAA guidelines using double-annotation followed by arbitration, rounds of sanity checking, and proof reading. The average token-based F1 measure for the annotators compared to the gold standard was 0.927. The resulting annotations were used both to de-identify the data and to set the gold standard for the de-identification track of the 2014 i2b2/UTHealth shared task. All annotated private health information were replaced with realistic surrogates automatically and then read over and corrected manually. The resulting corpus is the first of its kind made available for de-identification research. This corpus was first used for the 2014 i2b2/UTHealth shared task, during which the systems achieved a mean F-measure of 0.872 and a maximum F-measure of 0.964 using entity-based micro-averaged evaluations.

Keywords

Natural language processing
HIPAA
De-identification
Annotation

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