Corpus ID: 202660640

Extracting evidence of supplement-drug interactions from literature

@article{Wang2019ExtractingEO,
  title={Extracting evidence of supplement-drug interactions from literature},
  author={Lucy Lu Wang and Oyvind Tafjord and Sarthak Jain and Arman Cohan and Sam Skjonsberg and Carissa Schoenick and Nick Botner and Waleed Ammar},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.08135}
}
Dietary supplements are used by a large portion of the population, but information on their safety is hard to find. [...] Key Method We fine-tune the contextualized word representations of BERT-large using labeled data from the PDDI corpus. We then process 22M abstracts from PubMed using this model, and extract evidence for 55946 unique interactions between 1923 supplements and 2727 drugs (precision: 0.77, recall: 0.96), demonstrating that learning the task of DDI classification transfers successfully to the…Expand

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References

SHOWING 1-10 OF 48 REFERENCES
Discovery and Explanation of Drug-Drug Interactions via Text Mining
TLDR
This work trains a random forest classifier to score potential DDIs based on the features of the normalized assertions extracted from the literature that relate two drugs to a gene product, and shows how the classifier can be used to explain known DDIs and to uncover new DDIs that have not yet been reported. Expand
Extracting drug-drug interactions from literature using a rich feature-based linear kernel approach
TLDR
This work proposes an efficient and scalable system using a linear kernel to identify DDI information and demonstrates 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. Expand
Drug drug interaction extraction from the literature using a recursive neural network
TLDR
A recursive neural network model uses a position feature, a subtree containment feature, and an ensemble method to improve the performance of DDI extraction and demonstrates that this model can automatically extract DDIs better than existing models. Expand
Discovering drug–drug interactions: a text-mining and reasoning approach based on properties of drug metabolism
TLDR
A novel approach that integrates text mining and automated reasoning to derive DDIs is proposed that can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions. Expand
Toward a complete dataset of drug-drug interaction information from publicly available sources
TLDR
It is thought that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. Expand
A linguistic rule-based approach to extract drug-drug interactions from pharmacological documents
TLDR
This work proposes the first integral solution for the automatic extraction of DDI from biomedical texts using a hybrid linguistic approach that combines shallow parsing and syntactic simplification with pattern matching. Expand
Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey
TLDR
This study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking, and suggests greater automation and standardization across search strategies could improve one’s ability to identify PDDIevidence. Expand
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
TLDR
Experimental results showed that the proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure. Expand
Classification of use status for dietary supplements in clinical notes
  • Yadan Fan, Lu He, Rui Zhang
  • Computer Science, Medicine
  • 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
  • 2016
TLDR
It is demonstrated that the classifier can accurately classify supplement usage status, which can be further integrated as a module into the existing natural language processing pipeline for supporting dietary supplement knowledge discovery. Expand
Using natural language processing methods to classify use status of dietary supplements in clinical notes
  • Yadan Fan, Rui Zhang
  • Computer Science, Medicine
  • BMC Medical Informatics and Decision Making
  • 2018
TLDR
A comparison result shows that the machine learning-based classifier has a better performance, which is more efficient and scalable especially when the sample size doubles. Expand
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