language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: DUA
pretty_name: n2c2 2006 De-identification
homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
bigbio_pubmed: false
bigbio_public: false
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
Dataset Card for n2c2 2006 De-identification
Dataset Description
- Homepage: https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
- Pubmed: False
- Public: False
- Tasks: NER
The data for the de-identification challenge came from Partners Healthcare and included solely medical discharge summaries. We prepared the data for the challengeby annotating and by replacing all authentic PHI with realistic surrogates.
Given the above definitions, we marked the authentic PHI in the records in two stages. In the first stage, we used an automatic system.31 In the second stage, we validated the output of the automatic system manually. Three annotators, including undergraduate and graduate students and a professor, serially made three manual passes over each record. They marked and discussed the PHI tags they disagreed on and finalized these tags after discussion.
The original dataset does not have spans for each entity. The spans are computed in this loader and the final text that correspond with the tags is preserved in the source format
Citation Information
@article{uzuner2007evaluating,
author = {
Uzuner, Özlem and
Luo, Yuan and
Szolovits, Peter
},
title = {Evaluating the State-of-the-Art in Automatic De-identification},
journal = {Journal of the American Medical Informatics Association},
volume = {14},
number = {5},
pages = {550-563},
year = {2007},
month = {09},
url = {https://doi.org/10.1197/jamia.M2444},
doi = {10.1197/jamia.M2444},
eprint = {https://academic.oup.com/jamia/article-pdf/14/5/550/2136261/14-5-550.pdf}
}