Measuring Risk and Information Preservation: Toward New Metrics for De-identification of Clinical Texts

2nd Louhi Workshop on Text and Data Mining of Health Documents, Louhi 2010 - Proceedings

Volumn , Issue , 2010, Pages 72-75

  Hirschman, Lynette ^a   Aberdeen, John ^a  

^a MITRE CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

'CURRENT; CURRENT SYSTEM; DE-IDENTIFICATION; INFORMATION PRESERVATIONS; RE IDENTIFICATIONS; REAL-WORLD;

EID: 84983476625     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Beckwith, B. A., Mahaadevan, R., Balis, U. J., & Kuo, F. (2006). Development and evaluation of an open source software tool for deidentification of pathology reports. BMC Med Inform Decis Mak, 6, 12.
2. Berman, J. (2003). Concept-Match Medical Data Scrubbing. Arch Pathol Lab Med, 127, 680-686.
3. Friedlin, F. J., & McDonald, C. J. (2008). A software tool for removing patient identifying information from clinical documents. J Am Med Inform Assoc, 15(5), 601-610.
4. Grishman, R., & Sundheim, B. (1996). Message Understanding Conference - 6: A Brief History. Paper presented at the 16th International Conference on Computational Linguistics, Copenhagen.
5. Gupta, D., Saul, M., & Gilbertson, J. (2004). Evaluation of a deidentification (DE-ID) software engine to share pathology reports and clinical documents for research. Am J Clin Pathol, 121(2), 176-186.
6. Krallinger, M., Morgan, A., Smith, L., Leitner, F., Tanabe, L., Wilbur, J., Hirschman, L., & Valencia, A. (2008). Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge. Genome Biol, 9 Suppl 2, S1.
7. Machanavajjhala, A., Kifer, D., Gehrke, J., & Venkitasubramaniam, M. (2007). l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 3.
8. Malin, B. (2007). A computational model to protect patient data from location-based re-identification. Artif Intell Med, 40(3), 223-239.
9. Morrison, F. P., Li, L., Lai, A. M., & Hripcsak, G. (2009). Repurposing the clinical record: can an existing natural language processing system deidentify clinical notes? J Am Med Inform Assoc, 16(1), 37-39.
10. Savova, G., Kipper-Schuler, K., Buntrock, J., & Chute, C. (2008). UIMA-based Clinical Information Extraction System. Towards Enhanced Interoperability for Large HLT Systems: UIMA for NLP, 39.
11. Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty Fuzziness and Knowledge Based Systems, 10(5), 557-570.
12. Szarvas, G., Farkas, R., & Busa-Fekete, R. (2007). State-of-the-art anonymization of medical records using an iterative machine learning framework. J Am Med Inform Assoc, 14(5), 574-580.
13. Uzuner, Ö., Luo, Y., & Szolovits, P. (2007). Evaluating the state-of-the-art in automatic de-identification. J Am Med Inform Assoc, 14(5), 550-563.
14. Uzuner, Ö., Sibanda, T. C., Luo, Y., & Szolovits, P. (2008). A de-identifier for medical discharge summaries. Artif Intell Med, 42(1), 13-35.
15. Wellner, B., Huyck, M., Mardis, S., Aberdeen, J., Morgan, A., Peshkin, L., Yeh, A., Hitzeman, J., & Hirschman, L. (2007). Rapidly retargetable approaches to de-identification in medical records. J Am Med Inform Assoc, 14(5), 564-573.
16. Yeniterzi, R., Aberdeen, J., Bayer, S., Wellner, B., Clark, C., Hirschman, L., & Malin, B. (2010). Effects of Personal Identifier Resynthesis on Clinical Text De-identification. J Am Med Inform Assoc, 17(2), 159-168.