  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.
