Fuzzy Approach Topic Discovery in Health and Medical Corpora

Amir Karami, Aryya Gangopadhyay, Bin Zhou, Hadi Kharrazi

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


The majority of medical documents and electronic health records are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have been developed in recent years to make the text processing automatic. One of the popular approaches to retrieve information based on discovering the themes in health and medical corpora is topic modeling; however, this approach still needs new perspectives. In this research, we describe fuzzy latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy perspective. FLSA can handle health and medical corpora redundancy issue and provides a new method to estimate the number of topics. The quantitative evaluations show that FLSA produces superior performance and features to latent Dirichlet allocation, the most popular topic model.

Original languageEnglish (US)
Pages (from-to)1334-1345
Number of pages12
JournalInternational Journal of Fuzzy Systems
Issue number4
StatePublished - Apr 1 2018


  • Fuzzy approach
  • Health
  • Medical
  • Text mining
  • Topic model

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computational Theory and Mathematics
  • Artificial Intelligence


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