ArticleBMC bioinformatics2018
Biomedical event extraction based on GRU integrating attention mechanism.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.Scientific reports · 2025Article
- Exploiting question-answer framework with multi-GRU to detect adverse drug reaction on social media.Scientific reports · 2025Article
- Biomedical event argument detection method based on multi-feature fusion and question-answer paradigm.Heliyon · 2024Article
- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
- Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.JMIR medical informatics · 2022Article
- DeepMPM: a mortality risk prediction model using longitudinal EHR data.BMC bioinformatics · 2022Article
- A biomedical event extraction method based on fine-grained and attention mechanism.BMC bioinformatics · 2022Article
- A Structure-Based B-cell Epitope Prediction Model Through Combing Local and Global Features.Frontiers in immunology · 2022Article
- Event Scene Method of Legal Domain Knowledge Map Based on Neural Network Hybrid Model.Applied bionics and biomechanics · 2022Article
- Biomedical event extraction with a novel combination strategy based on hybrid deep neural networks.BMC bioinformatics · 2020Article
- Unsupervised inference of implicit biomedical events using context triggers.BMC bioinformatics · 2020Article
- Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations.BMC bioinformatics · 2019Article
- Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12Digital healthArticle
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4 authors.
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Abstract
backgroundBiomedical event extraction is a crucial task in biomedical text mining. As the primary forum for international evaluation of different biomedical event extraction technologies, BioNLP Shared Task represents a trend in biomedical text mining toward fine-grained information extraction (IE). The fourth series of BioNLP Shared Task in 2016 (BioNLP-ST'16) proposed three tasks, in which the Bacteria Biotope event extraction (BB) task has been put forward in the earlier BioNLP-ST. Deep learning methods provide an effective way to automatically extract more complex features and achieve notable results in various natural language processing tasks.
resultsThe experimental results show that the presented approach can achieve an F-score of 57.42% in the test set, which outperforms previous state-of-the-art official submissions to BioNLP-ST 2016.
conclusionsIn this paper, we propose a novel Gated Recurrent Unit Networks framework integrating attention mechanism for extracting biomedical events between biotope and bacteria from biomedical literature, utilizing the corpus from the BioNLP'16 Shared Task on Bacteria Biotope task. The experimental results demonstrate the potential and effectiveness of the proposed framework.
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