ArticleComputational and mathematical methods in medicine2020
A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Editorial: Methods and applications of natural language processing in psychiatry research.Frontiers in psychiatry · 2022Article
- Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The Effect of Posaconazole and Isavuconazole on the Pharmacokinetics of Erdafitinib in Beagle Dogs by UPLC-MS/MS.Frontiers in pharmacology · 2021Article
- A pathway-informed disease-related gene identification approach and its application to screen novel risk genes for Alzheimer's disease.Journal of Alzheimer's disease reportsArticle
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.
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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.