Detecting privacy-sensitive events in medical text

Prateek Jindal, Carl A. Gunter, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a novel semi-supervised technique for finding privacy-sensitive events in clinical text. Unlike traditional semi-supervised methods, we do not require large amounts of unannotated data. Instead, our approach relies on information contained in the hierarchical structure of a large medical encyclopedia.

Original languageEnglish (US)
Title of host publicationACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages617-620
Number of pages4
ISBN (Electronic)9781450328944
DOIs
StatePublished - Sep 20 2014
Event5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014 - Newport Beach, United States
Duration: Sep 20 2014Sep 23 2014

Publication series

NameACM BCB 2014 - 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM BCB 2014
Country/TerritoryUnited States
CityNewport Beach
Period9/20/149/23/14

Keywords

  • Active learning
  • Computer security
  • Concept identification
  • Domain knowledge
  • Health informatics
  • Natural language processing
  • SNOMED CT
  • Semi-supervision
  • Set expansion

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Software
  • Biomedical Engineering

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