FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents
One of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.
Digital Object Identifier (DOI)
2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), 2015.
© IEEE, 2015
Karami, A., Gangopadhyay, A., Zhou, B., Kharrazi, H. (2015). A Fuzzy approach model for uncovering hidden latent semantic structure in medical text collections. 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC). https://doi.org/10.1109/NAFIPS-WConSC.2015.7284190