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首页> 外文期刊>Journal of medical Internet research >Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model
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Detecting Potential Adverse Drug Reactions Using a Deep Neural Network Model

机译：使用深度神经网络模型检测潜在的药物不良反应

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BackgroundAdverse drug reactions (ADRs) are common and are the underlying cause of over a million serious injuries and deaths each year. The most familiar method to detect ADRs is relying on spontaneous reports. Unfortunately, the low reporting rate of spontaneous reports is a serious limitation of pharmacovigilance.ObjectiveThe objective of this study was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN).MethodsWe designed a DNN model that utilizes the chemical, biological, and biomedical information of drugs to detect ADRs. This model aimed to fulfill two main purposes: identifying the potential ADRs of drugs and predicting the possible ADRs of a new drug. For improving the detection performance, we distributed representations of the target drugs in a vector space to capture the drug relationships using the word-embedding approach to process substantial biomedical literature. Moreover, we built a mapping function to address new drugs that do not appear in the dataset.ResultsUsing the drug information and the ADRs reported up to 2009, we predicted the ADRs of drugs recorded up to 2012. There were 746 drugs and 232 new drugs, which were only recorded in 2012 with 1325 ADRs. The experimental results showed that the overall performance of our model with mean average precision at top-10 achieved is 0.523 and the rea under the receiver operating characteristic curve (AUC) score achieved is 0.844 for ADR prediction on the dataset.ConclusionsOur model is effective in identifying the potential ADRs of a drug and the possible ADRs of a new drug. Most importantly, it can detect potential ADRs irrespective of whether they have been reported in the past.
机译：背景药物不良反应（ADR）很常见，并且是每年造成一百万以上严重伤害和死亡的根本原因。检测ADR的最熟悉的方法是依靠自发报告。不幸的是，自发报告的报告率低是严重的药物警戒性。目的本研究的目的是确定一种使用深度神经网络（DNN）自动检测潜在药物不良反应的方法。用于检测ADR的药物的化学，生物和生物医学信息。该模型旨在实现两个主要目的：识别药物的潜在ADR和预测新药物的潜在ADR。为了提高检测性能，我们使用单词嵌入方法处理大量生物医学文献，在矢量空间中分布了目标药物的表示形式，以捕获药物关系。此外，我们构建了映射功能来处理未出现在数据集中的新药。结果利用截至2009年的药物信息和ADR报告，我们预测了截至2012年记录的药物ADR。共有746种药物和232种新药物，仅在2012年记录了1325个ADR。实验结果表明，在数据集上进行ADR预测时，该模型的平均性能在前10位的平均平均精度为0.523，并且在接收器工作特征曲线（AUC）得分下的面积为0.844。确定药物的潜在ADR和新药的潜在ADR。最重要的是，无论过去是否已报告，它都可以检测潜在的ADR。

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