ArticleJournal of biomedical informatics2015
Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews.BMC medical informatics and decision making · 2017Pooled it
- Dual channel drug-drug interactions extraction based on cross attention.BMC bioinformatics · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Drug-drug interaction identification using large language models.medRxiv : the preprint server for health sciences · 2025Article
- Biomedical relation extraction method based on ensemble learning and attention mechanism.BMC bioinformatics · 2024Article
- Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on COVID-19.Journal of medical Internet research · 2023Article
- Improving Drug-Drug Interaction Extraction with Gaussian Noise.Pharmaceutics · 2023Article
- Phar-LSTM: a pharmacological representation-based LSTM network for drug-drug interaction extraction.PeerJ · 2023Article
- DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction.Frontiers in digital health · 2023Article
- A sequence labeling framework for extracting drug-protein relations from biomedical literature.Database : the journal of biological databases and curation · 2022Article
- Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.Molecular diversity · 2021Article
- Extraction of causal relations based on SBEL and BERT model.Database : the journal of biological databases and curation · 2021Article
- A corpus-driven standardization framework for encoding clinical problems with HL7 FHIR.Journal of biomedical informatics · 2020Article
- Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug-Drug Interaction Information Retrieval.Frontiers in pharmacology · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition.Genomics & informatics · 2019Article
- Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing.Clinical pharmacology and therapeutics · 2019Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Drug-Drug Interaction Extraction via Recurrent Hybrid Convolutional Neural Networks with an Improved Focal Loss.Entropy (Basel, Switzerland) · 2019Article
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Authors and funding
4 authors.
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Abstract
Identifying unknown drug interactions is of great benefit in the early detection of adverse drug reactions. Despite existence of several resources for drug-drug interaction (DDI) information, the wealth of such information is buried in a body of unstructured medical text which is growing exponentially. This calls for developing text mining techniques for identifying DDIs. The state-of-the-art DDI extraction methods use Support Vector Machines (SVMs) with non-linear composite kernels to explore diverse contexts in literature. While computationally less expensive, linear kernel-based systems have not achieved a comparable performance in DDI extraction tasks. In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information. The proposed approach consists of two steps: identifying DDIs and assigning one of four different DDI types to the predicted drug pairs. We demonstrate that when equipped with a rich set of lexical and syntactic features, a linear SVM classifier is able to achieve a competitive performance in detecting DDIs. In addition, the one-against-one strategy proves vital for addressing an imbalance issue in DDI type classification. Applied to the DDIExtraction 2013 corpus, our system achieves an F1 score of 0.670, as compared to 0.651 and 0.609 reported by the top two participating teams in the DDIExtraction 2013 challenge, both based on non-linear kernel methods.
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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.