{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fuzzy-approach-topic-discovery-in-health-and","title":"Fuzzy Approach Topic Discovery in Health and Medical Corpora","arxiv_id":"1705.00995","date":"2017-05-02","proceeding":null,"authors":["Amir Karami","Aryya Gangopadhyay","Bin Zhou","Hadi Kharrazi"],"abstract":"The majority of medical documents and electronic health records (EHRs) are in\ntext format that poses a challenge for data processing and finding relevant\ndocuments. Looking for ways to automatically retrieve the enormous amount of\nhealth and medical knowledge has always been an intriguing topic. Powerful\nmethods have been developed in recent years to make the text processing\nautomatic. One of the popular approaches to retrieve information based on\ndiscovering the themes in health & medical corpora is topic modeling, however,\nthis approach still needs new perspectives. In this research we describe fuzzy\nlatent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy\nperspective. FLSA can handle health & medical corpora redundancy issue and\nprovides a new method to estimate the number of topics. The quantitative\nevaluations show that FLSA produces superior performance and features to latent\nDirichlet allocation (LDA), the most popular topic model.","url_abs":"http://arxiv.org/abs/1705.00995v2","url_pdf":"http://arxiv.org/pdf/1705.00995v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fuzzy-approach-topic-discovery-in-health-and","repo_url":"https://github.com/amir-karami/Health-News-Tweets-Data","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}