ArticleProceedings. IEEE International Conference on Healthcare Informatics2017
Extracting Drug-Drug Interactions with Word and Character-Level Recurrent Neural Networks.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 67 citations in OpenAlex.
- A parallel dual-stream state-space module for reliable and efficient biomedical relation extraction.PLoS computational biology · 2026Article
- Dual channel drug-drug interactions extraction based on cross attention.BMC bioinformatics · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- LitTx: A New Treatment Relation Extraction Dataset.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2026Article
- Comparison of pipelines, seq2seq models, and LLMs for rare disease information extraction.Natural language processing and information systems : ... International Conference on Applications of Natural Language to Information Systems, NLDB ... revised papers. International Conference on Applications of Natural Language to Info... · 2026Article
- Enhanced drug-drug interaction extraction from biomedical text using deep learning-based sentence representations.Scientific reports · 2025Article
- Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations.Database : the journal of biological databases and curation · 2023Article
- End-to-EndProceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- MSEDDI: Multi-Scale Embedding for Predicting Drug-Drug Interaction Events.International journal of molecular sciences · 2023Article
- Explainable Artificial Intelligence for Predictive Modeling in Healthcare.Journal of healthcare informatics research · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.Frontiers in bioengineering and biotechnology · 2022Article
- Detecting Personal Medication Intake in Twitter via Domain Attention-Based RNN with Multi-Level Features.Computational intelligence and neuroscience · 2022Article
- The risk of racial bias while tracking influenza-related content on social media using machine learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.ACM transactions on computing for healthcare · 2021Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations.BMC bioinformatics · 2019Article
- Distant supervision for treatment relation extraction by leveraging MeSH subheadings.Artificial intelligence in medicine · 2019Article
- Confirm or refute?: A comparative study on citation sentiment classification in clinical research publications.Journal of biomedical informatics · 2019Article
- Generalizing biomedical relation classification with neural adversarial domain adaptation.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Drug-drug interactions (DDIs) are known to be responsible for nearly a third of all adverse drug reactions. Hence several current efforts focus on extracting signal from EMRs to prioritize DDIs that need further exploration. To this end, being able to extract explicit mentions of DDIs in free text narratives is an important task. In this paper, we explore recurrent neural network (RNN) architectures to detect and classify DDIs from unstructured text using the DDIExtraction dataset from the SemEval 2013 (task 9) shared task. Our methods are in line with those used in other recent deep learning efforts for relation extraction including DDI extraction. However, to our knowledge, we are the first to investigate the potential of character-level RNNs (Char-RNNs) for DDI extraction (and relation extraction in general). Furthermore, we explore a simple but effective model bootstrapping method to (a). build model averaging ensembles, (b). derive confidence intervals around mean micro-F scores (MMF), and (c). assess the average behavior of our methods. Without any rule based filtering of negative examples, a popular heuristic used by most earlier efforts, we achieve an MMF of 69.13. By adding simple replicable heuristics to filter negative instances we are able to achieve an MMF of 70.38. Furthermore, our best ensembles produce micro F-scores of 70.81 (without filtering) and 72.13 (with filtering), which are superior to metrics reported in published results. Although Char-RNNs turnout to be inferior to regular word based RNN models in overall comparisons, we find that ensembling models from both architectures results in nontrivial gains over simply using either alone, indicating that they complement each other.
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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.