University of Illinois Urbana-Champaign

A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections

Karami, Amir; Gangopadhyay, Aryya; Zhou, Bin; Kharrazi, Hadi

Content Files
456_ready.pdf

Permalink

https://hdl.handle.net/2142/73755

Description

Title
A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections
Author(s)
  • Karami, Amir
  • Gangopadhyay, Aryya
  • Zhou, Bin
  • Kharrazi, Hadi
Issue Date
2015-03-15
Keyword(s)
  • natural language processing
  • health informtics
  • text/data/knowledge mining
Date of Ingest
2015-03-24T16:07:44Z
Abstract
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.
Publisher
iSchools
Series/Report Name or Number
iConference 2015 Proceedings
Type of Resource
text
Genre of Resource
Conference Poster
Language
English
Permalink
http://hdl.handle.net/2142/73755
Copyright and License Information
Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

Owning Collections

iConference 2015 Poster Descriptions PRIMARY