@InProceedings{yadav-EtAl:2016:ClinicalNLP,
  author    = {Yadav, Shweta  and  Ekbal, Asif  and  Saha, Sriparna  and  Bhattacharyya, Pushpak},
  title     = {Deep Learning Architecture for Patient Data De-identification in Clinical Records},
  booktitle = {Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {32--41},
  abstract  = {Rapid growth in Electronic Medical Records (EMR) has emerged to an expansion of
	data in the
	clinical domain. The majority of the available health care information is
	sealed in the form of narrative
	documents which form the rich source of clinical information. Text mining of
	such clinical
	records has gained huge attention in various medical applications like
	treatment and decision making.
	However, medical records enclose patient Private Health Information (PHI) which
	can
	reveal the identities of the patients. In order to retain the privacy of
	patients, it is mandatory to remove
	all the PHI information prior to making it publicly available. The aim is to
	de-identify or
	encrypt the PHI from the patient medical records. In this paper, we propose an
	algorithm based
	on deep learning architecture to solve this problem. We perform
	de-identification of seven PHI
	terms from the clinical records. Experiments on benchmark datasets show that
	our proposed
	approach achieves encouraging performance, which is better than the baseline
	model developed
	with Conditional Random Field.},
  url       = {http://aclweb.org/anthology/W16-4206}
}