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iDrug: Text mining driven international adverse drug-drug interactions
iDrug: 텍스트마이닝 기반 약물 상호작용 분석

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Authors
김세정
Advisor
윤성로
Major
공과대학 전기·정보공학부
Issue Date
2016-02
Publisher
서울대학교 대학원
Keywords
Text miningAdverse drug-drug interaction
Description
학위논문 (석사)-- 서울대학교 대학원 : 전기·정보공학부, 2016. 2. 윤성로.
Abstract
Abstract
iDrug: Text mining driven international adverse drug-drug interactions

Saejung Kim
Electrical and Computer Engineering
The Graduate School
Seoul National University

With the remarkable development of medical analysis techniques, many drug-drug interactions (DDI) have been identified. Especially, as drugs with adverse DDIs can bring severe side-effects when used together, this information should be given with priority to medical experts in an emergency situation such as in intensive care unit (ICU). However, drug compendia that provide drug-drug interaction severity often have inconsistencies of DDI severity among different compendia. Therefore, it is critical to identify the correct DDI severity and organize such information in a consistent and structured way.

We applied text mining techniques to the extraction of drug-drug interactions from prescribing information in six countries (the United States, the United Kingdom, Canada, France, Switzerland, and Japan), and constructed a DDI severity database that stores two levels of DDI severity, contraindications and precautions, for the six countries. Based on the information we extracted, we analyzed the characteristics of the DDI network for each nation. We computed the pairwise differences of adverse DDIs between nations to understand the inconsistency levels of the DDIs of each country compared to the other countries. To the best of our knowledge, our work is the first attempt to automatically extract PI from the national regulatory authorities of the six countries by using text-mining techniques. Since our data were populated from PI approved by national regulatory authorities, we believe that our database can serve as a more authentic and reliable guide for medical experts.

keywords : Adverse drug-drug interaction, Text mining
Student Number : 2014-21735
Language
English
URI
https://hdl.handle.net/10371/122811
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses (Master's Degree_전기·정보공학부)
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