Wang Chi-Shiang, Lin Pei-Ju, Cheng Ching-Lan, Tai Shu-Hua, Kao Yang Yea-Huei, Chiang Jung-Hsien
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.
School of Pharmacy, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
J Med Internet Res. 2019 Feb 6;21(2):e11016. doi: 10.2196/11016.
Adverse 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.
The objective of this study was to identify a method to detect potential ADRs of drugs automatically using a deep neural network (DNN).
We 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.
Using 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.
Our 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.
药物不良反应（ADR）很常见，是每年导致超过一百万起严重伤害和死亡的根本原因。最常见的检测药物不良反应的方法是依靠自发报告。不幸的是，自发报告的低报告率是药物警戒的一个严重限制。
本研究的目的是确定一种使用深度神经网络（DNN）自动检测药物潜在不良反应的方法。
我们设计了一种DNN模型，该模型利用药物的化学、生物学和生物医学信息来检测不良反应。该模型旨在实现两个主要目的：识别药物的潜在不良反应和预测新药可能的不良反应。为了提高检测性能，我们使用词嵌入方法处理大量生物医学文献，将目标药物的表示分布在向量空间中以捕捉药物关系。此外，我们构建了一个映射函数来处理数据集中未出现的新药。
利用截至2009年报告的药物信息和不良反应，我们预测了截至2012年记录的药物的不良反应。有746种药物和232种新药，这些新药仅在2012年有记录，共有1325种不良反应。实验结果表明，我们的模型在数据集上进行不良反应预测时，前10名的平均平均精度的整体性能为0.523，接收器操作特征曲线（AUC）得分下的面积为0.844。
我们的模型在识别药物的潜在不良反应和新药可能的不良反应方面是有效的。最重要的是，它可以检测潜在的不良反应，无论它们过去是否已被报告。
