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KMID : 1155220180430040329
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Journal of the Korean Society of Health Information and Health Statistics
2018 Volume.43 No. 4 p.329 ~ p.335
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Analysis of Unstructured Data on Detecting of New Drug Indication of Atorvastatin
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Jeong Hwee-Soo
Kang Gil-Won
Choi Woong
Park Jong-Hyock
Shin Kwang-Soo
Suh Young-Sung
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Abstract
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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.
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KEYWORD
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Text mining, Network analysis, Atorvastatin, Indication
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