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The majority of medical documents and electronic health records (EHRs) are intext format that poses a challenge for data processing and finding relevantdocuments. Looking for ways to automatically retrieve the enormous amount ofhealth and medical knowledge has always been an intriguing topic. Powerfulmethods have been developed in recent years to make the text processingautomatic. One of the popular approaches to retrieve information based ondiscovering the themes in health & medical corpora is topic modeling, however,this approach still needs new perspectives. In this research we describe fuzzylatent semantic analysis (FLSA), a novel approach in topic modeling using fuzzyperspective. FLSA can handle health & medical corpora redundancy issue andprovides a new method to estimate the number of topics. The quantitativeevaluations show that FLSA produces superior performance and features to latentDirichlet allocation (LDA), the most popular topic model.

Fuzzy Approach Topic Discovery in Health and Medical Corpora