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首页> 外文期刊>The Journal of Infectious Diseases >Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health
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Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health

机译:数字药物警戒和疾病监测:结合传统和大数据系统以改善公共卫生

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摘要

The digital revolution has contributed to very large data sets (ie, big data) relevant for public health. The two major data sources are electronic health records from traditional health systems and patient-generated data. As the two data sources have complementary strengths-high veracity in the data from traditional sources and high velocity and variety in patient-generated data-they can be combined to build more-robust public health systems. However, they also have unique challenges. Patient-generated data in particular are often completely unstructured and highly context dependent, posing essentially a machine-learning challenge. Some recent examples from infectious disease surveillance and adverse drug event monitoring demonstrate that the technical challenges can be solved. Despite these advances, the problem of verification remains, and unless traditional and digital epidemiologic approaches are combined, these data sources will be constrained by their intrinsic limits.
机译:数字革命为与公共卫生相关的超大型数据集(即大数据)做出了贡献。两个主要数据源是来自传统卫生系统的电子健康记录和患者生成的数据。由于这两个数据源具有互补的优势-传统数据源的数据准确性高,患者生成的数据速度快且种类繁多,因此可以将它们组合起来以构建更强大的公共卫生系统。但是,它们也有独特的挑战。尤其是患者生成的数据通常是完全非结构化的,并且高度依赖上下文,这对机器学习构成了挑战。来自传染病监测和药物不良事件监测的一些最新实例表明,可以解决技术难题。尽管取得了这些进展,但验证的问题仍然存在,除非将传统的和数字的流行病学方法结合起来,否则这些数据源将受到其固有限制的约束。

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