# Fuzzy Approach Topic Discovery in Health and Medical Corpora

arXiv: https://arxiv.org/abs/1705.00995 · HTML: https://mlsanity.com/paper/1705.00995/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 5 of 5)

- [Determination of the Number of Topics Intrinsically: Is It Possible?](https://mlsanity.com/paper/2406.10402/) (2024)
- [COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter](https://mlsanity.com/paper/2108.04816/) (2021)
- [Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness](https://mlsanity.com/paper/2209.11762/) (2022)
- [#REVAL: a semantic evaluation framework for hashtag recommendation](https://mlsanity.com/paper/2305.18330/) (2023)
- [Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data](https://mlsanity.com/paper/2506.04230/) (2025)

## Abstract (arXiv)

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.