FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents
This work addresses the problem of retrieving relevant documents in medical domains, but it is incremental as it applies fuzzy set theory to improve existing topic modeling methods.
The authors tackled the challenge of analyzing large-scale medical documents by developing a novel topic modeling approach using fuzzy clustering, which achieved better performance than LDA on two medical text datasets in document classification and modeling tasks.
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.