Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Chung Chun Lee; Seunghee Lee; Mi Hwa Song; Suehyun Lee; Jong Yeop Kim

doi:10.1109/BigComp57234.2023.00067

Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Chung Chun Lee, Seunghee Lee, Mi Hwa Song, Suehyun Lee, Jong Yeop Kim

Research output: Contribution to book or proceeding › Conference article › peer-review

Abstract

Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Editors	Hyeran Byun, Beng Chin Ooi, Katsumi Tanaka, Sang-Won Lee, Zhixu Li, Akiyo Nadamoto, Giltae Song, Young-guk Ha, Kazutoshi Sumiya, Wu Yuncheng, Hyuk-Yoon Kwon, Takehiro Yamamoto
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	322-323
Number of pages	2
ISBN (Electronic)	9781665475785
DOIs	https://doi.org/10.1109/BigComp57234.2023.00067
State	Published - Feb 1 2023
Externally published	Yes
Event	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 - Jeju, Korea, Republic of Duration: Feb 13 2023 → Feb 16 2023

Publication series

Name	2023 IEEE International Conference on Big Data and Smart Computing (BigComp)

Conference

Conference	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Country/Territory	Korea, Republic of
City	Jeju
Period	02/13/23 → 02/16/23

Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Information Systems
Information Systems and Management
Statistics, Probability and Uncertainty
Health Informatics

Keywords

ADR
classification
concept
pharmacovigilance
pipeline
SNS

Access to Document

10.1109/BigComp57234.2023.00067

Cite this

Lee, C. C., Lee, S., Song, M. H., Lee, S., & Kim, J. Y. (2023). Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach. In H. Byun, B. C. Ooi, K. Tanaka, S.-W. Lee, Z. Li, A. Nadamoto, G. Song, Y. Ha, K. Sumiya, W. Yuncheng, H.-Y. Kwon, & T. Yamamoto (Eds.), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 (pp. 322-323). (2023 IEEE International Conference on Big Data and Smart Computing (BigComp)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp57234.2023.00067

Lee, Chung Chun ; Lee, Seunghee ; Song, Mi Hwa et al. / Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. editor / Hyeran Byun ; Beng Chin Ooi ; Katsumi Tanaka ; Sang-Won Lee ; Zhixu Li ; Akiyo Nadamoto ; Giltae Song ; Young-guk Ha ; Kazutoshi Sumiya ; Wu Yuncheng ; Hyuk-Yoon Kwon ; Takehiro Yamamoto. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 322-323 (2023 IEEE International Conference on Big Data and Smart Computing (BigComp)).

@inproceedings{48655e1ac0c5469793f2f509e77cca82,

title = "Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach",

abstract = "Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87\% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80\%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.",

keywords = "ADR, classification, concept, pharmacovigilance, pipeline, SNS",

author = "Lee, \{Chung Chun\} and Seunghee Lee and Song, \{Mi Hwa\} and Suehyun Lee and Kim, \{Jong Yeop\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",

year = "2023",

month = feb,

day = "1",

doi = "10.1109/BigComp57234.2023.00067",

language = "English",

series = "2023 IEEE International Conference on Big Data and Smart Computing (BigComp)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "322--323",

editor = "Hyeran Byun and Ooi, \{Beng Chin\} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

address = "United States",

}

Lee, CC, Lee, S, Song, MH, Lee, S & Kim, JY 2023, Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach. in H Byun, BC Ooi, K Tanaka, S-W Lee, Z Li, A Nadamoto, G Song, Y Ha, K Sumiya, W Yuncheng, H-Y Kwon & T Yamamoto (eds), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. 2023 IEEE International Conference on Big Data and Smart Computing (BigComp), Institute of Electrical and Electronics Engineers Inc., pp. 322-323, 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Jeju, Korea, Republic of, 02/13/23. https://doi.org/10.1109/BigComp57234.2023.00067

Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach. / Lee, Chung Chun; Lee, Seunghee; Song, Mi Hwa et al.
Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. ed. / Hyeran Byun; Beng Chin Ooi; Katsumi Tanaka; Sang-Won Lee; Zhixu Li; Akiyo Nadamoto; Giltae Song; Young-guk Ha; Kazutoshi Sumiya; Wu Yuncheng; Hyuk-Yoon Kwon; Takehiro Yamamoto. Institute of Electrical and Electronics Engineers Inc., 2023. p. 322-323 (2023 IEEE International Conference on Big Data and Smart Computing (BigComp)).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

AU - Lee, Chung Chun

AU - Lee, Seunghee

AU - Song, Mi Hwa

AU - Lee, Suehyun

AU - Kim, Jong Yeop

PY - 2023/2/1

Y1 - 2023/2/1

N2 - Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.

AB - Social Network Services (SNS) is currently the most active communication platform, generating big data every day. In the field of pharmacovigilance, it is also an interesting research topic to extract meaningful information from accumulated data from SNS data. In this work, we propose a Recurrent Neural Network (RNN) based classification model to extract Adver Drug Reactions (ADR) posts from social network service (SNS) data. For Ketoprofen drugs with high prescription frequency and high number of posts, posts from Naver Blog and Cafe (2005-2020) were secured, and the final 3,828 cases were analyzed. As a result, three types of lexicons (drugs name, ADR, and stop words) were defined for Ketoprofen, and based on this, 87% accuracy was obtained based on the Bi-LSTM classification model. Extra drug was also verified through the entire process above, with an accuracy of 80%. It is expected to provide convenience in extracting ADR posts through the developed Bi-LSTM classification model.

KW - ADR

KW - classification

KW - concept

KW - pharmacovigilance

KW - pipeline

KW - SNS

UR - https://www.scopus.com/pages/publications/85151565315

U2 - 10.1109/BigComp57234.2023.00067

DO - 10.1109/BigComp57234.2023.00067

M3 - Conference article

AN - SCOPUS:85151565315

T3 - 2023 IEEE International Conference on Big Data and Smart Computing (BigComp)

SP - 322

EP - 323

BT - Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

A2 - Byun, Hyeran

A2 - Ooi, Beng Chin

A2 - Tanaka, Katsumi

A2 - Lee, Sang-Won

A2 - Li, Zhixu

A2 - Nadamoto, Akiyo

A2 - Song, Giltae

A2 - Ha, Young-guk

A2 - Sumiya, Kazutoshi

A2 - Yuncheng, Wu

A2 - Kwon, Hyuk-Yoon

A2 - Yamamoto, Takehiro

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

Y2 - 13 February 2023 through 16 February 2023

ER -

Lee CC, Lee S, Song MH, Lee S, Kim JY. Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach. In Byun H, Ooi BC, Tanaka K, Lee SW, Li Z, Nadamoto A, Song G, Ha Y, Sumiya K, Yuncheng W, Kwon HY, Yamamoto T, editors, Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 322-323. (2023 IEEE International Conference on Big Data and Smart Computing (BigComp)). doi: 10.1109/BigComp57234.2023.00067

Adverse Drug Reaction Posts Detection with a Bi-LSTM based approach

Abstract

Publication series

Conference

Scopus Subject Areas

Keywords

Access to Document

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