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.

中文翻译：

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用于从 AERS 报告中提取药物间相互作用的改进 Skip-Gram 算法。


药物相互作用（DDI）是导致不良事件反应不可或缺的因素之一。考虑到AERS（美国食品药品监督管理局不良事件报告系统（FDA AERS））报告的独特结构，我们改变了原始skip-gram算法中窗口值的范围，提出了一种语言概念表示模型并提取了药物特征来自大型 AERS 报告的姓名和反应信息。我们的方案的有效性通过十倍交叉验证中与源自共现矩阵的向量进行比较来测试和验证。在DrugBank DDI数据库的描述富集验证中，计算了测量的准确性。基于所提出的语言模型的逻辑回归分类器的接受者操作特征曲线下的平均面积比共现矩阵高6%。同时，五类严重不良事件的平均准确率为 88%。这些结果表明我们的语言模型可用于从大规模 AERS 报告中提取药物和反应特征。