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Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions

Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions

Shiho Kitajima, Rafal Rzepka, Kenji Araki
Copyright: © 2014 |Volume: 5 |Issue: 2 |Pages: 17
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781466655621|DOI: 10.4018/ijmdem.2014040101
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MLA

Kitajima, Shiho, et al. "Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions." IJMDEM vol.5, no.2 2014: pp.1-17. http://doi.org/10.4018/ijmdem.2014040101

APA

Kitajima, S., Rzepka, R., & Araki, K. (2014). Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions. International Journal of Multimedia Data Engineering and Management (IJMDEM), 5(2), 1-17. http://doi.org/10.4018/ijmdem.2014040101

Chicago

Kitajima, Shiho, Rafal Rzepka, and Kenji Araki. "Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions," International Journal of Multimedia Data Engineering and Management (IJMDEM) 5, no.2: 1-17. http://doi.org/10.4018/ijmdem.2014040101

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Abstract

Obtaining medical information has a beneficial influence on patients' treatment and QOL (quality of life). The authors aim to make a system that helps patients to collect narrative information. Extracting information from data written by patients will allow the acquisition of information which is easy to understand and provides encouragement. Additionally, by using large-scale data, the system can be utilized for discovering unknown effects or patterns. As the first step, the purpose of this paper is to extract descriptions of the effects caused by taking drugs as a triplet of expressions from illness survival blogs' snippets. This paper proposes a method to extract the triplets using specific clue words and parsing the results in order to extract from blogs written in free natural language. Moreover, recall was improved by combining their proposed method and a baseline system, and precision was improved by filtering using dictionaries we created from existing medical documents.

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