Extracting adverse drug events (ADEs) is one of the most challenging tasks in biomedical field. However, the existing studies fail to fully utilize the ...

Abstract—Extracting adverse drug events (ADEs) is one of the most challenging tasks in biomedical field. However, the existing studies fail to fully utilize ...

Mar 31, 2017 · The ADE task aims to extract two kinds of entities (drugs and diseases) and relations about which drug is associated with which disease (ADEs).

Experiments show that the masking mechanism benefits the proposed model by improving the accuracy from 72% to 77%. In addition, a catering knowledge base is ...

A span-based neural model treats the task as a relation triplets prediction problem, and builds the entity-graph by enumerating all possible candidate spans ...

Aug 7, 2019 · We proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state ...

We propose a novel transition-based framework for extracting entities and event mentions jointly as a single task. The entity information is the basis for ...

Oct 21, 2021 · Miwa and Bansal [9] proposed an end-to-end joint relation extraction model where they stacked bidirectional tree-structured LSTMs on ...

Sep 16, 2022 · The model performance using GNN models shows the efficiency in extracting and summarizing the drug's features from the network structure.

Li et al. exploited a transition-based feed forward neural network to jointly extract drug-disease entity mentions and their adverse drug event relations [45] .