Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.
Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.
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DOI：
10.3390/ijerph18052600
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发表时间：
2021-03-05
影响因子：
--
通讯作者：
Dixit AC
中科院分区：
文献类型：
--
作者：
Anastopoulos IN;Herczeg CK;Davis KN;Dixit AC
虽然临床批准过程能够过滤出其效用不能抵消其在人体中的不良药物反应特征的药物，但它不太适合表征可能发生在真实的世界不同患者人群中的低频率问题和特异质多药物相互作用。随着包含数十万患者记录的真实世界证据数据库越来越丰富，现在可以构建机器学习模型，将个体患者信息纳入其中，以提供个性化的不良事件预测。在这项研究中，我们建立了模型，将患者特定的人口统计学，临床和遗传特征（当可用时）与药物结构相结合，以预测药物不良反应。我们开发了一种可扩展的图卷积方法，能够从典型患者可能服用的药物的可变数量中整合分子效应。我们的模型在预测英国生物银行数据集中的住院和死亡的任务方面优于标准机器学习方法，分别产生0.37的R2和0.90的AUC。我们相信我们的模型有潜力在真实的世界不同人群中评估新的治疗化合物的个体化毒性。它也可以用来优先考虑药物时，有多个选项正在考虑治疗。
While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.
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