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Title:Detecting Privacy-Sensitive Events in Medical Text
Author(s):Jindal, Prateek; Roth, Dan; Gunter, Carl A.
Subject(s):Natural Language Processing (NLP)
Electronic Health Records
Mention Detection
Set-Expansion
SNOMED CT
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.
Issue Date:2013-09-24
Genre:Technical Report
Type:Text
Language:English
URI:http://hdl.handle.net/2142/45819
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Sponsor:HHS 90TR0003/01
Rights Information:Copyright 2013 Prateek Jindal
Date Available in IDEALS:2013-09-24


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