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 .
Approach: They propose a multimodal adverse drug event detection dataset that merges ADE-related textual information with visual aids to enhance patient safety.
Outcome: The proposed dataset integrates ADE-related textual information with visual aids to improve patient safety and healthcare accessibility.

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A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events (2020.findings-emnlp)

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Challenge: Adverse drug events (ADEs) are a leading cause of death in the United States and cost around $30 $130 billion every year.
Approach: They propose a multi-grained joint deep network to learn ADE entity recognition and ADE sentence classification tasks.
Outcome: The proposed model improves state-of-art F1 score on the MADE 1.0 benchmark of EHR notes.
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)

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Challenge: Using NLP methods to discover and extract adverse drug events from unstructured textual data is difficult because it requires time-consuming manual curation.
Approach: They propose to use a hierarchical event schema to extract annotated events from medical case reports and biomedical literature to analyze patient data.
Outcome: The proposed dataset is the largest public dataset to date and contains over 5000 events from medical case reports and biomedical literature.
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance (2023.findings-emnlp)

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Challenge: pharmacovigilance (PV) is a tool for analyzing adverse drug events from biomedical literature . pharmacologists use natural language processing to extract core information from papers .
Approach: They propose a resource for biomedical adverse drug event eXtraction using natural language processing.
Outcome: The proposed model achieves 59.1% F1 (validation) and estimates human performance to be 72.0% F1 . the proposed model could be used to improve drug safety monitoring, also called pharmacovigilance, in the future.
A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages (2024.lrec-main)

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Challenge: Existing clinical corpora mostly revolves around scientific articles in English . existing literature is limited to only a few scientific articles .
Approach: They propose to use user-generated data sources to uncover adverse drug reactions . existing clinical corpora mostly revolves around scientific articles in english . authors provide statistics to highlight certain challenges associated with the corpus .
Outcome: The proposed corpus includes 12 entity types, four attribute types, and 13 relation types . it provides strong baselines for extracting entities and relations between entities .
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.
Approach: They propose to integrate medical knowledge into ADE detection from text . they use contextualized embeddings from pretrained language models and convolutional graph neural networks to learn features differently for different types of nodes in the graph.
Outcome: The proposed model outperforms existing models on four public datasets and shows that it is based on medical knowledge and embeddings from pretrained language models and neural networks.
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.
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.
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)

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Challenge: pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects.
Approach: They investigate the ability of large language models (LLMs) to extract adverse events from medical text.
Outcome: The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models.
Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings (2025.acl-long)

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Challenge: Existing resources for AE extraction are limited due to complexity, variability, and ambiguity of clinical narratives.
Approach: They present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients.
Outcome: The proposed model performs well on coarse-grained extraction, but drops notably for rare events and complex attributes.
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)

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Challenge: Adverse Events (AEs) are harmful events resulting from the use of medical products.
Approach: They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness.
Outcome: The proposed approach achieves strong performance over baselines on English benchmarks.

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