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Research Journal of Pharmacy and Technology
Year : 2017, Volume : 10, Issue : 7
First page : ( 2313) Last page : ( 2321)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2017.00410.3

The Methodology of Answer filtering through online healthcare Q&A Community

Ryu Sung-Hyun*, Choi Sang-Hyun**

Dept. Management Information Systems, Chungbuk National University, Korea

*Corresponding Author E-mail: rsh1451@cbnu.ac.kr

**Chois@cbnu.ac.kr

Online published on 17 October, 2017.

Abstract

Background/Objectives

With the advent of Web 2.0, a low quality is overflowing in reality. Because human life is connected directly with healthcare, it is important to manage and assess the information.

Methods/Statistical analysis

In this paper, we have analyzed and classified answers for knowledge customers to get high-quality information by using medical noun list in Naver Knowledge in which is a Q&A community site. We gathered 784 questions and 1542 answers.

Findings

The result of accuracy of classification is that ‘Naive Bayes’ records 46% and matching status scores 60%. It shows that library about lung cancer we developed could be used to filter worthless knowledge whenever knowledge consumer wants to get useful medical information. Our development of keyword library is different from the existing medical library in that we consider keywords of non-expert and expert about medical information.

Improvements/Applications

Experimental results contribute to filter the healthcare information for people who are easily seduce by wrong medical information.

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Keywords

Lung cancer, text mining, medical term, healthcare, answer filtering.

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