Author/Authors :
Wang, Li Department of Medical Informatics - Medical School - Nantong University - Nantong, China , Pan, Wenjie Department of Medical Informatics - Medical School - Nantong University - Nantong, China , Wang, QingHua Department of Medical Informatics - Medical School - Nantong University - Nantong, China , Bai, Heming Nantong University - Nantong, China , Liu, Wei Nantong University - Nantong, China , Jiang, Lei Department of Rheumatology and Immunology - Changzheng Hospital - The Second Military Medical University - Shanghai, China , Zhang, Yuanpeng Department of Medical Informatics - Medical School - Nantong University - Nantong, China
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.
Keywords :
Skip-Gram , Drug-Drug , AERS , DDI
