论文标题
数据挖掘和分析模型，以预测和确定不良药物的相互作用
Data Mining and Analytical Models to Predict and Identify Adverse Drug-drug Interactions
论文作者
论文摘要
使用多种药物占所有医院入院的30％，是美国第五大死亡原因。由于认为所有不良药物事件（ADE）中有30％以上是由药物相互作用（DDI）引起的，因此更好地识别和预测初级和二级护理中已知DDIS的给药可以减少医院寻求紧急护理的患者数量，从而使全球范围内的卫生系统大量节省。但是，当前的DDI预测模型容易使偏见以及不准确或缺乏从电子健康记录（EHR）获得纵向数据（EHR）和其他药物信息（例如FDA不良事件报告系统（FAER）（FAERS））的主要障碍，这些障碍仍然是衡量DDI和医疗保健表征的主要障碍。在这篇综述中，讨论了使用药物副作用数据和监督学习DDI预测模型（DGIS）数据（DGIS）数据的分析模型。与以前的版本相比，这两个模型中对DDI的识别的改进都被突出显示，而包括偏见，不准确性和数据不足的局限性也得到了评估。研究了使用随机森林分类器DGI数据对牛皮癣DDI预测的案例研究。在未来的工作中讨论了解决上述限制的转移矩阵复发性神经网络（TM-RNN）。
The use of multiple drugs accounts for almost 30% of all hospital admission and is the 5th leading cause of death in America. Since over 30% of all adverse drug events (ADEs) are thought to be caused by drug-drug interactions (DDI), better identification and prediction of administration of known DDIs in primary and secondary care could reduce the number of patients seeking urgent care in hospitals, resulting in substantial savings for health systems worldwide along with better public health. However, current DDI prediction models are prone to confounding biases along with either inaccurate or a lack of access to longitudinal data from Electronic Health Records (EHR) and other drug information such as FDA Adverse Event Reporting System (FAERS) which continue to be the main barriers in measuring the prevalence of DDI and characterizing the phenomenon in medical care. In this review, analytical models including Label Propagation using drug side effect data and Supervised Learning DDI Prediction model using Drug-Gene interactions (DGIs) data are discussed. Improved identification of DDIs in both of these models compared to previous versions are highlighted while limitations that include bias, inaccuracy, and insufficient data are also assessed. A case study of Psoriasis DDI prediction by DGI data using Random Forest Classifier was studied. Transfer Matrix Recurrent Neural Networks (TM-RNN) that address the above limitations are discussed in future works.
