Syntactic Type-aware Graph Attention Network for Drug-drug Interactions and their Adverse Effects Extraction-Reference-Cited by-同舟云学术

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Syntactic Type-aware Graph Attention Network for Drug-drug Interactions and their Adverse Effects Extraction

Published:2022-12-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
language:
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Author:

Chen Peng¹,Wang Jian¹,Lin Hongfei¹,Wang Yichen²,Zhao Di³,Zhang Yijia⁴

Affiliation:

1. Dalian University of Technology,School of Computer Science and Technology,Dalian,China

2. Dalian Kexiang Technology Development Co.Ltd,Dalian,China

3. Dalian Minzu University,School of Computer Science and Engineering,Dalian,China

4. Dalian Maritime University,School of Information Science and Technology,Dalian,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9994793/9994847/09995045.pdf?arnumber=9995045

Reference22 articles.

1. Hospitalisations and emergency department visits due to drug–drug interactions: a literature review

2. Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports

3. Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013);Segura-Bedmar

4. A neural joint model for entity and relation extraction from biomedical text

5. End-to-End Neural Relation Extraction with Global Optimization

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1. CARE-30: A Causally Driven Multi-Modal Model for Enhanced 30-Day ICU Readmission Predictions;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

2. Positive Sample Augmentation with Graph Mixup for DDI Prediction;2023 IEEE International Conference on Medical Artificial Intelligence (MedAI);2023-11-18

3. Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning;Bioinformatics;2023-08-01

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