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International Journal of Fuzzy Systems

, Volume 20, Issue 4, pp 1334–1345 | Cite as

Fuzzy Approach Topic Discovery in Health and Medical Corpora

  • Amir Karami
  • Aryya Gangopadhyay
  • Bin Zhou
  • Hadi Kharrazi
Article

Abstract

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.

Keywords

Text mining Topic model Medical Health Fuzzy approach 

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Library and Information ScienceUniversity of South CarolinaColumbiaUSA
  2. 2.Information Systems DepartmentUniversity of Maryland Baltimore CountyBaltimoreUSA
  3. 3.Bloomberge School of Public HealthJohns Hopkins UniversityBaltimoreUSA

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