Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-9
Number of pages5
ISBN (Electronic)9781728112299
DOIs
Publication statusPublished - Dec 24 2018
Event2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 - Taichung, Taiwan
Duration: Nov 30 2018Dec 2 2018

Publication series

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

Conference

Conference2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018
CountryTaiwan
CityTaichung
Period11/30/1812/2/18

Fingerprint

Classifiers
Marketing
Health
Monitoring

Keywords

  • Adverse drug reaction
  • Imbalanced classification
  • Social media mining

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

Cite this

Wang, C. K., Dai, H. J., Wang, F. D., & Su, E. C. Y. (2018). Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques. In Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018 (pp. 5-9). [8588467] (Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI.2018.00011

Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques. / Wang, Chen Kai; Dai, Hong Jie; Wang, Feng Duo; Su, Emily Chia Yu.

Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 5-9 8588467 (Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, CK, Dai, HJ, Wang, FD & Su, ECY 2018, Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques. in Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018., 8588467, Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 5-9, 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018, Taichung, Taiwan, 11/30/18. https://doi.org/10.1109/TAAI.2018.00011
Wang CK, Dai HJ, Wang FD, Su ECY. Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques. In Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 5-9. 8588467. (Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018). https://doi.org/10.1109/TAAI.2018.00011
Wang, Chen Kai ; Dai, Hong Jie ; Wang, Feng Duo ; Su, Emily Chia Yu. / Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques. Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 5-9 (Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018).
@inproceedings{f6c156e1eec04e968aa7910dab8cbaa2,
title = "Adverse Drug Reaction Post Classification with Imbalanced Classification Techniques",
abstract = "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.",
keywords = "Adverse drug reaction, Imbalanced classification, Social media mining",
author = "Wang, {Chen Kai} and Dai, {Hong Jie} and Wang, {Feng Duo} and Su, {Emily Chia Yu}",
year = "2018",
month = "12",
day = "24",
doi = "10.1109/TAAI.2018.00011",
language = "English",
series = "Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5--9",
booktitle = "Proceedings - 2018 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2018",
address = "United States",

}

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

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

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.

ER -