Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing

Seth Polsley, Atif Tahir, Muppala Raju, Akintayo Akinleye, Duane Steward


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.
Anthology ID:
W17-2324
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
194–199
Language:
URL:
https://www.aclweb.org/anthology/W17-2324
DOI:
10.18653/v1/W17-2324
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2324.pdf