Fuzzy Approach Topic Discovery in Health and Medical Corpora
This work addresses the problem of automatically retrieving information from health and medical corpora for researchers and practitioners, but it is incremental as it builds on existing topic modeling approaches.
The paper tackles the challenge of processing medical text data by proposing a fuzzy latent semantic analysis (FLSA) method for topic modeling, which shows superior performance compared to latent Dirichlet allocation (LDA) in quantitative evaluations.
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.