ArticleBMC bioinformatics2014
Knowledge-based extraction of adverse drug events from biomedical text.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Towards discovery: an end-to-end system for uncovering novel biomedical relations.Database : the journal of biological databases and curation · 2024Article
- LPInsider: a webserver for lncRNA-protein interaction extraction from the literature.BMC bioinformatics · 2022Article
- Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study.JMIR medical informatics · 2021Article
- Identification of Adverse Drug Event-Related Japanese Articles: Natural Language Processing Analysis.JMIR medical informatics · 2020Article
- Exploiting sequence labeling framework to extract document-level relations from biomedical texts.BMC bioinformatics · 2020Article
- Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Adverse drug reaction detection via a multihop self-attention mechanism.BMC bioinformatics · 2019Article
- Drug-Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities.Smart health (Amsterdam, Netherlands) · 2018Article
- Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.BMC medical informatics and decision making · 2018Article
- Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.JMIR public health and surveillance · 2018Article
- Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.JMIR medical informatics · 2017Article
- A neural joint model for entity and relation extraction from biomedical text.BMC bioinformatics · 2017Article
- Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records.Journal of healthcare engineering · 2017Article
- Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.Database : the journal of biological databases and curation · 2016Article
- Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task.Database : the journal of biological databases and curation · 2016Article
- Extraction of chemical-induced diseases using prior knowledge and textual information.Database : the journal of biological databases and curation · 2016Article
- Chemical-induced disease relation extraction with various linguistic features.Database : the journal of biological databases and curation · 2016Article
- Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text.Database : the journal of biological databases and curation · 2016Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany biomedical relation extraction systems are machine-learning based and have to be trained on large annotated corpora that are expensive and cumbersome to construct. We developed a knowledge-based relation extraction system that requires minimal training data, and applied the system for the extraction of adverse drug events from biomedical text. The system consists of a concept recognition module that identifies drugs and adverse effects in sentences, and a knowledge-base module that establishes whether a relation exists between the recognized concepts. The knowledge base was filled with information from the Unified Medical Language System. The performance of the system was evaluated on the ADE corpus, consisting of 1644 abstracts with manually annotated adverse drug events. Fifty abstracts were used for training, the remaining abstracts were used for testing.
resultsThe knowledge-based system obtained an F-score of 50.5%, which was 34.4 percentage points better than the co-occurrence baseline. Increasing the training set to 400 abstracts improved the F-score to 54.3%. When the system was compared with a machine-learning system, jSRE, on a subset of the sentences in the ADE corpus, our knowledge-based system achieved an F-score that is 7 percentage points higher than the F-score of jSRE trained on 50 abstracts, and still 2 percentage points higher than jSRE trained on 90% of the corpus.
conclusionA knowledge-based approach can be successfully used to extract adverse drug events from biomedical text without need for a large training set. Whether use of a knowledge base is equally advantageous for other biomedical relation-extraction tasks remains to be investigated.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.