Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France.
Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
Sci Rep. 2018 Oct 22;8(1):15558. doi: 10.1038/s41598-018-33980-0.
Prescribing the right drug with the right dose is a central tenet of precision medicine. We examined the use of patients' prior Electronic Health Records to predict a reduction in drug dosage. We focus on drugs that interact with the P450 enzyme family, because their dosage is known to be sensitive and variable. We extracted diagnostic codes, conditions reported in clinical notes, and laboratory orders from Stanford's clinical data warehouse to construct cohorts of patients that either did or did not need a dose change. After feature selection, we trained models to predict the patients who will (or will not) require a dose change after being prescribed one of 34 drugs across 23 drug classes. Overall, we can predict (AUC ≥ 0.70-0.95) a dose reduction for 23 drugs and 22 drug classes. Several of these drugs are associated with clinical guidelines that recommend dose reduction exclusively in the case of adverse reaction. For these cases, a reduction in dosage may be considered as a surrogate for an adverse reaction, which our system could indirectly help predict and prevent. Our study illustrates the role machine learning may take in providing guidance in setting the starting dose for drugs associated with response variability.
精准医学的核心原则是开具正确剂量的正确药物。我们研究了利用患者的电子健康记录来预测药物剂量减少的情况。我们专注于与 P450 酶家族相互作用的药物，因为已知这些药物的剂量敏感且多变。我们从斯坦福临床数据仓库中提取诊断代码、临床记录中报告的病症和实验室订单，构建了需要或不需要剂量调整的患者队列。在进行特征选择后，我们训练模型来预测在开处方的 34 种药物中的一种药物后，患者是否（或不）需要剂量调整。总的来说，我们可以预测（AUC≥0.70-0.95）23 种药物和 22 种药物类别中的剂量减少。其中一些药物与临床指南相关联，这些指南建议在出现不良反应的情况下减少剂量。在这些情况下，减少剂量可以被视为不良反应的替代指标，我们的系统可以间接帮助预测和预防不良反应。我们的研究说明了机器学习在为与反应变异性相关的药物设定起始剂量提供指导方面可能发挥的作用。
