ArticleBMC bioinformatics2017
A neural joint model for entity and relation extraction from biomedical text.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Dual channel drug-drug interactions extraction based on cross attention.BMC bioinformatics · 2026Article
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.Briefings in bioinformatics · 2024Review
- BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network.BMC bioinformatics · 2023Article
- A Joint Extraction System Based on Conditional Layer Normalization for Health Monitoring.Sensors (Basel, Switzerland) · 2023Article
- A metric learning-based method for biomedical entity linking.Frontiers in research metrics and analytics · 2023Article
- JCBIE: a joint continual learning neural network for biomedical information extraction.BMC bioinformatics · 2022Article
- An automatic hypothesis generation for plausible linkage between xanthium and diabetes.Scientific reports · 2022Article
- A biomedical event extraction method based on fine-grained and attention mechanism.BMC bioinformatics · 2022Article
- Explainable detection of adverse drug reaction with imbalanced data distribution.PLoS computational biology · 2022Article
- Characteristics of the 100 largest modern zoonotic disease outbreaks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- A sui generis QA approach using RoBERTa for adverse drug event identification.BMC bioinformatics · 2021Article
- MT-clinical BERT: scaling clinical information extraction with multitask learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- HeTDR: Drug repositioning based on heterogeneous networks and text mining.Patterns (New York, N.Y.) · 2021Article
- Construction of Genealogical Knowledge Graphs From Obituaries: Multitask Neural Network Extraction System.Journal of medical Internet research · 2021Article
- Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.ACM transactions on computing for healthcare · 2021Article
- Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.Scientific reports · 2021Article
- KGen: a knowledge graph generator from biomedical scientific literature.BMC medical informatics and decision making · 2020Article
- Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning.Journal of biomedical informatics · 2020Article
- Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.JMIR medical informatics · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExtracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above.
resultsOur model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction.
conclusionsThe proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.
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