1) i2b2 2006 Deidentification and Smoking Challenge's data set:
NLP Data Set #1B: 889 de-identified discharge summaries with
de-identification challenge annotations, training and test sets and
Please cite as:
Uzuner Ö., Juo Y, Szolovits P. "Evaluating the state-of-the-art in
automatic de-identification". J Am Med Inform Assoc. 2007,
14(5):550-63. http://www.jamia.org/cgi/content/abstract/14/5/550 .
2) i2b2 NLP Data Set #7a: De-identification Challenge Data Set:
Please cite as:
Stubbs A, Uzuner Ö. (2015). "Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus". J
Biomed Inform. 2015 Aug 28. pii: S1532-0464(15)00182-3. doi:
10.1016/j.jbi.2015.07.020. [Epub ahead of print]. http://www.ncbi.nlm.nih.gov/pubmed/26319540.
Stubbs A, Kotfila C, Uzuner Ö. (2015). "Automated systems for the de-identification of longitudinal clinical narratives: Overview of
2014 i2b2/UTHealth shared task Track 1". J Biomed Inform. 2015 Jul 28.
pii: S1532-0464(15)00117-3. doi: 10.1016/j.jbi.2015.06.007. [Epub
ahead of print]. http://www.ncbi.nlm.nih.gov/pubmed/26225918.
3) i2b2 2016 (mirror) CEGS N-GRID challenge; Track 1: De-identification.
The deid software package was developed and tested using a gold standard corpus of 2,434 nursing notes that have been thoroughly deidentified by a multi-pass process that included meticulous reviews by three or more experts working independently, as well as by a variety of automated methods. All detected instances of PHI in these nursing notes have been replaced by realistic surrogate data in the gold standard corpus. Although the deid software, as noted above, may be redistributed under the terms of the GPL, the gold standard corpus, because of the very small possibility that it may contain one or more instances of undetected PHI, is currently available only under terms that do not permit it to be redistributed.
- Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and Physionet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13).
- Neamatullah I, Douglass M, Lehman LH, Reisner A, Villarroel M, Long WJ, Szolovits P, Moody GB, Mark RG, Clifford GD. Automated De-Identification of Free-Text Medical Records. BMC Medical Informatics and Decision Making, 2008, 8:32. doi:10.1186/1472-6947-8-32
In case anyone is interested, we presented an overview of the state-of-the art results on these two datasets in: