  The majority of medical documents and electronic health records (EHRs) 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 & 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 & 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 (LDA), the most popular topic model.
