FLATM: A fuzzy logic approach topic model for medical documents

Amir Karami, Aryya Gangopadhyay, Bin Zhou, Hadi Karrazi

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

Abstract

One of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.

Original languageEnglish (US)
Title of host publication2015 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467372473
DOIs
StatePublished - Sep 29 2015
EventAnnual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2015 - Redmond, United States
Duration: Aug 17 2015Aug 19 2015

Publication series

NameAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
Volume2015-September

Conference

ConferenceAnnual Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2015
CountryUnited States
CityRedmond
Period8/17/158/19/15

Keywords

  • Analytical models
  • Bioinformatics
  • Biomedical imaging
  • Computational modeling
  • Data models
  • Fuzzy set theory
  • Medical services

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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