TY - GEN
T1 - Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques
AU - Wang, Chen Kai
AU - Dai, Hong Jie
AU - Wang, Feng Duo
AU - Su, Emily Chia Yu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/24
Y1 - 2018/12/24
N2 - 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.
AB - 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.
KW - Adverse drug reaction
KW - Imbalanced classification
KW - Social media mining
UR - http://www.scopus.com/inward/record.url?scp=85061430787&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061430787&partnerID=8YFLogxK
U2 - 10.1109/TAAI.2018.00011
DO - 10.1109/TAAI.2018.00011
M3 - Conference contribution
AN - SCOPUS:85061430787
T3 - Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
SP - 5
EP - 9
BT - Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
Y2 - 30 November 2018 through 2 December 2018
ER -