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Found 7 papers, 3 papers with code
AILAB-Udine@SMM4H’22: Limits of Transformers and BERT Ensembles
This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task.
Boosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts
Biomedical entity linking, also known as biomedical concept normalization, has recently witnessed the rise to prominence of zero-shot contrastive models.
Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction
Adverse Event (ADE) extraction is one of the core tasks in digital pharmacovigilance, especially when applied to informal texts.
Generalizing over Long Tail Concepts for Medical Term Normalization
Medical term normalization consists in mapping a piece of text to a large number of output classes.
AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task.
Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.
NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations.
