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

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

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

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

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

Language

English

Permalink

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

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Copyright 2015 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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