Fan Yadan, Adam Terrence J, McEwan Reed, Pakhomov Serguei V, Melton Genevieve B, Zhang Rui
Institute for Health Informatics, Minneapolis, MN, USA.
Academic Health Center-Information Systems, Minneapolis, MN, USA.
Stud Health Technol Inform. 2017;245:370-374.
Drug and supplement interactions (DSIs) have drawn widespread attention due to their potential to affect therapeutic response and adverse event risk. Electronic health records provide a valuable source where the signals of DSIs can be identified and characterized. We detected signals of interactions between warfarin and seven dietary supplements, viz., alfalfa, garlic, ginger, ginkgo, ginseng, St. John's Wort, and Vitamin E by analyzing structured clinical data and unstructured clinical notes from the University of Minnesota Clinical Data Repository. A machine learning-based natural language processing module was further developed to classify supplement use status and applied to filter out irrelevant clinical notes. Cox proportional hazards models were fitted, controlling for a set of confounding factors: age, gender, and Charlson Index of Comorbidity. There was a statistically significant association of warfarin concurrently used with supplements which can potentially increase the risk of adverse events, such as gastrointestinal bleeding.
药物与补充剂相互作用(DSIs)因其影响治疗反应和不良事件风险的可能性而受到广泛关注。电子健康记录提供了一个有价值的来源,从中可以识别和表征DSIs信号。我们通过分析明尼苏达大学临床数据存储库中的结构化临床数据和非结构化临床记录,检测了华法林与七种膳食补充剂之间的相互作用信号,这七种膳食补充剂分别是苜蓿、大蒜、生姜、银杏、人参、圣约翰草和维生素E。进一步开发了基于机器学习的自然语言处理模块来分类补充剂使用状态,并应用于筛选出不相关的临床记录。拟合了Cox比例风险模型,控制了一组混杂因素:年龄、性别和查尔森合并症指数。华法林与可能增加不良事件风险(如胃肠道出血)的补充剂同时使用存在统计学上的显著关联。