FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents

TL;DR

This paper introduces FLATM, a fuzzy logic-based topic model for medical documents that improves document retrieval accuracy by utilizing fuzzy clustering, outperforming traditional LDA in medical text analysis.

Contribution

The paper presents a novel fuzzy clustering approach to topic modeling specifically tailored for large-scale medical documents, enhancing retrieval performance.

Findings

FLATM outperforms LDA in medical document classification

Fuzzy set theory improves topic modeling accuracy in medical domains

Experimental results validate the effectiveness of the proposed model

Abstract

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.