Papers with ADR
Abstract-level Deductive Reasoning for Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Existing methods fine-tune PLMs using the validity label and instance-level reasoning proofs as supervision signals. |
| Approach: | They propose to train PLMs to learn general reasoning patterns rather than instance-level knowledge by predicting the abstract reasoning proof of each sample. |
| Outcome: | The proposed model significantly reduces the impact of learning instance-level knowledge (over 70%) |
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
| Challenge: | Existing dictionary-based, semi-supervised learning approaches are limited by the coverage and maintainability of laymen health vocabularies. |
| Approach: | They propose a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset and generate a small set of labeled training sets. |
| Outcome: | The proposed approach matches the performance of fully-supervised approaches while requiring only 25% of training data. |
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. |
| Approach: | They propose to use binary annotated documents from a german patient forum to detect ADRs. |
| Outcome: | The proposed model achieves an F1 score of 37.52 for the positive class on the German patient forum. |
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining (P19-1)
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| Challenge: | Existing methods of identifying ADRs are reliable but time-consuming and offer a limited amount of ADR relevant information. |
| Approach: | They propose a neural network-inspired multi-task learning framework that can simultaneously extract ADRs from various sources. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three publicly available real-world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. |