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    <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.</abstract>
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%0 Conference Proceedings
%T Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing
%A Polsley, Seth
%A Tahir, Atif
%A Raju, Muppala
%A Akinleye, Akintayo
%A Steward, Duane
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F polsley-etal-2017-role
%X 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.
%R 10.18653/v1/W17-2324
%U https://aclanthology.org/W17-2324
%U https://doi.org/10.18653/v1/W17-2324
%P 194-199

Markdown (Informal)

[Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing](https://aclanthology.org/W17-2324) (Polsley et al., BioNLP 2017)

• Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing (Polsley et al., BioNLP 2017)

ACL

• Seth Polsley, Atif Tahir, Muppala Raju, Akintayo Akinleye, and Duane Steward. 2017. Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing. In BioNLP 2017, pages 194–199, Vancouver, Canada,. Association for Computational Linguistics.