Automated classification of computer-based medical device recalls: An application of natural language processing and statistical learning

Homa Alemzadeh, Raymond Hoagland, Zbigniew Kalbarczyk, Ravishankar K. Iyer

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

Abstract

This paper presents MedSafe, a framework for automated classification of computer-based medical device recalls. The data is collected from the U.S. Food and Drug Administration (FDA) recalls database. We combined techniques in natural language processing and statistical learning to automatically identify the computer-related recalls, by interpreting the natural language semantics of recall descriptions. We evaluated MedSafe on over 16K recall records submitted to the FDA between years 2007-2013.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE 27th International Symposium on Computer-Based Medical Systems, CBMS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages553-554
Number of pages2
ISBN (Print)9781479944354
DOIs
StatePublished - 2014
Event27th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2014 - New York, NY, United States
Duration: May 27 2014May 29 2014

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
ISSN (Print)1063-7125

Other

Other27th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2014
Country/TerritoryUnited States
CityNew York, NY
Period5/27/145/29/14

Keywords

  • Computer-related Failures
  • FDA Recalls
  • Medical Devices
  • Natural Language Processing
  • Statistical Learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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