A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections
One of the challenges for text analysis in the medical domain including the clinical notes and research papers is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult and previous work has also shown unique problems of medical documents. The themes in documents help to retrieve documents on the same topic with and without a query. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. In this paper we describe a novel approach in topic modeling, FATM, using fuzzy clustering. To assess the value of FATM, we experiment with two text datasets of medical documents. The quantitative evaluation carried out through log-likelihood on held-out data shows that FATM produces superior performance to LDA. This research contributes to the emerging field of understanding the characteristics of the medical documents and how to account for them in text mining.
Proceedings of the iConference 2015, 2015.
© The Authors, 2015
Karami A., Gangopadhyay A., Zhou, B., Kharrazi H. (2015), A Fuzzy approach model for uncovering hidden latent semantic structure in medical text collections. Proceedings of the iConference 2015, Newport Beach, CA. http://hdl.handle.net/2142/73755