Deep Learning Architecture for Patient Data De-identification in Clinical Records

Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya


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.
Anthology ID:
W16-4206
Volume:
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
ClinicalNLP | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
32–41
Language:
URL:
https://aclanthology.org/W16-4206
DOI:
Bibkey:
Cite (ACL):
Shweta Yadav, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya. 2016. Deep Learning Architecture for Patient Data De-identification in Clinical Records. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 32–41, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Deep Learning Architecture for Patient Data De-identification in Clinical Records (Yadav et al., 2016)
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PDF:
https://aclanthology.org/W16-4206.pdf