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아토바스타틴의 새로운 약물 적응증 탐색을 위한 비정형 데이터 분석

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Author(s)
Hwee-Soo JeongGil-Won KangWoong ChoiJong-Hyock ParkKwang-Soo ShinYoung-Sung Suh
Keimyung Author(s)
Suh, Young Sung
Department
Dept. of Family Medicine (가정의학)
Journal Title
보건정보통계학회지
Issued Date
2018
Volume
43
Issue
4
Keyword
Text miningNetwork analysisAtorvastatinIndication
Abstract
Objectives:
In recent years, there has been an increased need for a way to extract desired information from multiple medical literatures at once. This study was conducted to confirm the usefulness of unstructured data analysis using previously published medical literatures to search for new indications.

Methods:
The new indications were searched through text mining, network analysis, and topic modeling analysis using 5,057 articles of atorvastatin, a treatment for hyperlipidemia, from 1990 to 2017.

Results:
The extracted keywords was 273. In the frequency of text mining and network analysis, the existing indications of atorvastatin were extracted in top level. The novel indications by Term Frequency-Inverse Document Frequency (TF-IDF) were atrial fibrillation, heart failure, breast cancer, rheumatoid arthritis, combined hyperlipidemia, arrhythmias, multiple sclerosis, non-alcoholic fatty liver disease, contrast-induced acute kidney injury and prostate cancer.

Conclusions:
Unstructured data analysis for discovering new indications from massive medical literature is expected to be used in drug repositioning industries.
Alternative Title
Analysis of Unstructured Data on Detecting of New Drug Indication of Atorvastatin
Keimyung Author(s)(Kor)
서영성
Publisher
School of Medicine (의과대학)
Citation
Hwee-Soo Jeong et al. (2018). 아토바스타틴의 새로운 약물 적응증 탐색을 위한 비정형 데이터 분석. 보건정보통계학회지, 43(4), 329–335. doi: 10.21032/jhis.2018.43.4.329
Type
Article
ISSN
2465-8022
Source
https://e-jhis.org/journal/view.php?doi=10.21032/jhis.2018.43.4.329
DOI
10.21032/jhis.2018.43.4.329
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/42242
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Family Medicine (가정의학)
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