@InProceedings{polsley-EtAl:2017:BioNLP17,
  author    = {Polsley, Seth  and  Tahir, Atif  and  Raju, Muppala  and  Akinleye, Akintayo  and  Steward, Duane},
  title     = {Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {194--199},
  abstract  = {Electronic medical records (EMR) have largely replaced hand-written patient
	files in healthcare. The growing pool of EMR data presents a significant
	resource in medical research, but the U.S. Health Insurance Portability and
	Accountability Act (HIPAA) mandates redacting medical records before performing
	any analysis on the same. This process complicates obtaining medical data and
	can remove much useful information from the record. As part of a larger project
	involving ontology-driven medical processing, we employ a method of recognizing
	protected health information (PHI) that maps to ontological terms. We then use
	the relationships defined in the ontology to redact medical texts so that roles
	and semantics of terms are retained without compromising anonymity. The method
	is evaluated by clinical experts on several hundred medical documents,
	achieving up to a 98.8% f-score, and has already shown promise for retaining
	semantic information in later processing.},
  url       = {http://www.aclweb.org/anthology/W17-2324}
}