Bidirectional Long Short-Term Memory–Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches
Chung-Chun Lee, Seunghee Lee, Mi-Hwa Song, Jong-Yeup Kim, Suehyun Lee · 2024
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Description
Chung-Chun Lee and co-authors train a neural network to spot posts about drug side effects in Korean social media. Their 2024 study starts from a pain gel widely used by older patients. Early warning of bad reactions often hides in blogs and forums before it reaches official channels.
Posts were gathered from a big Korean portal across fifteen years. Pairs of drug names and reaction words were mined to build clean training data. A two-way memory network classified the posts, reaching 85 percent accuracy for the main drug. A second drug scored 80 percent. The authors offer the pipeline for other languages and drugs.
Catalog record
| CREATOR | Chung-Chun Lee, Seunghee Lee, Mi-Hwa Song, Jong-Yeup Kim, Suehyun Lee |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 26 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |