Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques

Chen Kai Wang, Hong Jie Dai, Feng Duo Wang, Emily Chia Yu Su

研究成果: 書貢獻/報告類型會議貢獻

4 引文 斯高帕斯(Scopus)

摘要

Nowadays, social media is often being used by users to create public messages related to their health. With the increasing number of social media usage, a trend has been observed of users creating posts related to adverse drug reactions (ADR). Mining social media data for these information can be used for pharmacological post-marketing surveillance and monitoring. However, the development of automatic ADR detection systems remains challenging because the corpora compiled from real world social media were usually highly imbalanced resulting in barriers to develop classifiers with reliable performance. In this work, we implemented a variety of imbalanced techniques and compared their performance on two large imbalanced data sets released for the purpose of detecting ADR posts. Comparing with state-of-the-art approaches developed for the two dataset, based on much less features, the developed classifiers with implemented imbalanced classification techniques achieved comparable or even better F-scores.
原文英語
主出版物標題Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5-9
頁數5
ISBN(電子)9781728112299
DOIs
出版狀態已發佈 - 12月 24 2018
事件2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 - Taichung, 台灣
持續時間: 11月 30 201812月 2 2018

出版系列

名字Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018

會議

會議2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
國家/地區台灣
城市Taichung
期間11/30/1812/2/18

ASJC Scopus subject areas

  • 電腦科學應用
  • 人工智慧

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