Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data
Abstract As society continues to age, it is becoming increasingly important to monitor drug use in the elderly. Social media data have been used for monitoring adverse drug reactions. The aim of this study was to determine whether social network studies (SNS) are useful sources of drug side effects...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-03-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28912-6 |
| _version_ | 1827984206126383104 |
|---|---|
| author | Seunghee Lee Hyekyung Woo Chung Chun Lee Gyeongmin Kim Jong-Yeup Kim Suehyun Lee |
| author_facet | Seunghee Lee Hyekyung Woo Chung Chun Lee Gyeongmin Kim Jong-Yeup Kim Suehyun Lee |
| author_sort | Seunghee Lee |
| collection | DOAJ |
| description | Abstract As society continues to age, it is becoming increasingly important to monitor drug use in the elderly. Social media data have been used for monitoring adverse drug reactions. The aim of this study was to determine whether social network studies (SNS) are useful sources of drug side effects information. We propose a method for utilizing SNS data to plot the known side effects of geriatric drugs in a dosing map. We developed a lexicon of drug terms associated with side effects and mapped patterns from social media data. We confirmed that well-known side effects may be obtained by utilizing SNS data. Based on these results, we propose a pharmacovigilance pipeline that can be extended to unknown side effects. We propose the standard analysis pipeline Drug_SNSMiner for monitoring side effects using SNS data and evaluated it as a drug prescription platform for the elderly. We confirmed that side effects may be monitored from the consumer’s perspective based on SNS data using only drug information. SNS data were deemed good sources of information to determine ADRs and obtain other complementary data. We established that these learning data are invaluable for AI requiring the acquisition of ADR posts on efficacious drugs. |
| first_indexed | 2024-04-09T22:57:20Z |
| format | Article |
| id | doaj.art-72df96ed7cf84811b866cd362f197915 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T22:57:20Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-72df96ed7cf84811b866cd362f1979152023-03-22T11:12:55ZengNature PortfolioScientific Reports2045-23222023-03-0113111010.1038/s41598-023-28912-6Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS dataSeunghee Lee0Hyekyung Woo1Chung Chun Lee2Gyeongmin Kim3Jong-Yeup Kim4Suehyun Lee5Healthcare Data Science Center, Konyang University HospitalDepartment of Health Administration, Kongju National UniversityDepartment of Biomedical Informatics, College of Medicine, Konyang UniversityDepartment of Biomedical Engineering, Konyang UniversityHealthcare Data Science Center, Konyang University HospitalCollege of IT Convergence, Gachon UniversityAbstract As society continues to age, it is becoming increasingly important to monitor drug use in the elderly. Social media data have been used for monitoring adverse drug reactions. The aim of this study was to determine whether social network studies (SNS) are useful sources of drug side effects information. We propose a method for utilizing SNS data to plot the known side effects of geriatric drugs in a dosing map. We developed a lexicon of drug terms associated with side effects and mapped patterns from social media data. We confirmed that well-known side effects may be obtained by utilizing SNS data. Based on these results, we propose a pharmacovigilance pipeline that can be extended to unknown side effects. We propose the standard analysis pipeline Drug_SNSMiner for monitoring side effects using SNS data and evaluated it as a drug prescription platform for the elderly. We confirmed that side effects may be monitored from the consumer’s perspective based on SNS data using only drug information. SNS data were deemed good sources of information to determine ADRs and obtain other complementary data. We established that these learning data are invaluable for AI requiring the acquisition of ADR posts on efficacious drugs.https://doi.org/10.1038/s41598-023-28912-6 |
| spellingShingle | Seunghee Lee Hyekyung Woo Chung Chun Lee Gyeongmin Kim Jong-Yeup Kim Suehyun Lee Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data Scientific Reports |
| title | Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data |
| title_full | Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data |
| title_fullStr | Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data |
| title_full_unstemmed | Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data |
| title_short | Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data |
| title_sort | drug snsminer standard pharmacovigilance pipeline for detection of adverse drug reaction using sns data |
| url | https://doi.org/10.1038/s41598-023-28912-6 |
| work_keys_str_mv | AT seungheelee drugsnsminerstandardpharmacovigilancepipelinefordetectionofadversedrugreactionusingsnsdata AT hyekyungwoo drugsnsminerstandardpharmacovigilancepipelinefordetectionofadversedrugreactionusingsnsdata AT chungchunlee drugsnsminerstandardpharmacovigilancepipelinefordetectionofadversedrugreactionusingsnsdata AT gyeongminkim drugsnsminerstandardpharmacovigilancepipelinefordetectionofadversedrugreactionusingsnsdata AT jongyeupkim drugsnsminerstandardpharmacovigilancepipelinefordetectionofadversedrugreactionusingsnsdata AT suehyunlee drugsnsminerstandardpharmacovigilancepipelinefordetectionofadversedrugreactionusingsnsdata |