Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches
Abstract BackgroundSocial networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug re...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-11-01
|
| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2024/1/e45289 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846151292294004736 |
|---|---|
| author | Chung-Chun Lee Seunghee Lee Mi-Hwa Song Jong-Yeup Kim Suehyun Lee |
| author_facet | Chung-Chun Lee Seunghee Lee Mi-Hwa Song Jong-Yeup Kim Suehyun Lee |
| author_sort | Chung-Chun Lee |
| collection | DOAJ |
| description |
Abstract
BackgroundSocial networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English.
ObjectiveA cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network.
MethodsIn previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, café posts, and NAVER Q&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac.
ResultsAmong the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85% and 80% accuracy, respectively.
ConclusionsHere, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible. |
| format | Article |
| id | doaj-art-f366dd532a084dcbb48f9ee723943774 |
| institution | Kabale University |
| issn | 2291-9694 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj-art-f366dd532a084dcbb48f9ee7239437742024-11-27T17:04:26ZengJMIR PublicationsJMIR Medical Informatics2291-96942024-11-0112e45289e4528910.2196/45289Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning ApproachesChung-Chun Leehttp://orcid.org/0000-0002-1670-6222Seunghee Leehttp://orcid.org/0000-0002-2273-0915Mi-Hwa Songhttp://orcid.org/0000-0001-7047-8032Jong-Yeup Kimhttp://orcid.org/0000-0003-1230-9307Suehyun Leehttp://orcid.org/0000-0003-0651-6481 Abstract BackgroundSocial networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English. ObjectiveA cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network. MethodsIn previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, café posts, and NAVER Q&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac. ResultsAmong the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85% and 80% accuracy, respectively. ConclusionsHere, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible.https://medinform.jmir.org/2024/1/e45289 |
| spellingShingle | Chung-Chun Lee Seunghee Lee Mi-Hwa Song Jong-Yeup Kim Suehyun Lee Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches JMIR Medical Informatics |
| title | Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches |
| title_full | Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches |
| title_fullStr | Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches |
| title_full_unstemmed | Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches |
| title_short | Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches |
| title_sort | bidirectional long short term memory based detection of adverse drug reaction posts using korean social networking services data deep learning approaches |
| url | https://medinform.jmir.org/2024/1/e45289 |
| work_keys_str_mv | AT chungchunlee bidirectionallongshorttermmemorybaseddetectionofadversedrugreactionpostsusingkoreansocialnetworkingservicesdatadeeplearningapproaches AT seungheelee bidirectionallongshorttermmemorybaseddetectionofadversedrugreactionpostsusingkoreansocialnetworkingservicesdatadeeplearningapproaches AT mihwasong bidirectionallongshorttermmemorybaseddetectionofadversedrugreactionpostsusingkoreansocialnetworkingservicesdatadeeplearningapproaches AT jongyeupkim bidirectionallongshorttermmemorybaseddetectionofadversedrugreactionpostsusingkoreansocialnetworkingservicesdatadeeplearningapproaches AT suehyunlee bidirectionallongshorttermmemorybaseddetectionofadversedrugreactionpostsusingkoreansocialnetworkingservicesdatadeeplearningapproaches |