Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks (P19-2)
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| Challenge: | Detection of adverse drug reactions in post-marketing period is a crucial challenge for pharmacology. |
| Approach: | They propose to use social media to extract information about adverse drug reactions . they compare four state-of-the-art attention-based neural networks to the F-measure . |
| Outcome: | The proposed methods perform better on four different benchmarks. |
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Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection (2024.lrec-main)
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| Challenge: | Recent studies have used word embedding and deep learning to automate ADE detection from text, but they did not incorporate explicit medical knowledge about drugs and adverse reactions or the corresponding feature learning. |
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A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages (2024.lrec-main)
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Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient’s Perspective (2022.lrec-1)
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| Challenge: | a recent study shows that the class labels of german documents containing ADRs are imbalanced . clinical trials and physicians prescribing medications cannot cover every potential use case. |
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Karel D’Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, Christopher Potts
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PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)
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Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He
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| Challenge: | ADEs are a serious public health concern and cost healthcare systems billions of dollars . despite advancements in healthcare, ADE detection remains a significant challenge . |
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| Challenge: | a study conducted by the pew Internet & American Life Project 1 shows that almost 80 percent of Internet users have explored health-related topic online. |
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Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content (D19-1)
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Sepideh Mesbah, Jie Yang, Robert-Jan Sips, Manuel Valle Torre, Christoph Lofi, Alessandro Bozzon, Geert-Jan Houben
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