# Fuzzy Approach Topic Discovery in Health and Medical Corpora

## TL;DR

This paper introduces Fuzzy Latent Semantic Analysis (FLSA), a novel fuzzy-based topic modeling method that improves health and medical document retrieval by addressing redundancy and estimating the number of topics more effectively than LDA.

## Contribution

The paper presents FLSA, a new fuzzy approach to topic modeling that enhances medical text analysis and outperforms traditional LDA in accuracy and feature extraction.

## Key findings

- FLSA outperforms LDA in quantitative evaluations.
- FLSA effectively handles redundancy in medical corpora.
- FLSA provides a new method for estimating the number of topics.

## Abstract

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.