Detecting Privacy-Sensitive Events in Medical Text

Jindal, Prateek; Roth, Dan; Gunter, Carl A.

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https://hdl.handle.net/2142/45819 Copy

Description

Title

Detecting Privacy-Sensitive Events in Medical Text

Author(s)

• Jindal, Prateek
• Roth, Dan
• Gunter, Carl A.

Issue Date

2013-09-24

Keyword(s)

• Natural Language Processing (NLP)
• Electronic Health Records
• Mention Detection
• Set-Expansion
• SNOMED CT

Date of Ingest

2013-09-24T15:07:26Z

Abstract

Recent US government initiatives have led to wide adoption of Electronic Health Records (EHRs). More and more health care institutions are storing patients' data in an electronic format. This emerging practice is posing several security-related risks because electronic data can easily be shared within and across institutions. So, it is important to design robust frameworks which will protect patients' privacy. In this report, we present a method to detect security-related (particularly drug abuse) events in medical text. Several applications can use this information to make the hospital systems more secure. For example, portions of the clinical reports which contain description of critical events can be encrypted so that it can be viewed only by selected individuals.

Type of Resource

text

Genre of Resource

Technical Report

Language

en

Permalink

http://hdl.handle.net/2142/45819

Sponsor(s)/Grant Number(s)

HHS 90TR0003/01

Copyright and License Information

Copyright 2013 Prateek Jindal

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