On the Convergence of Epidemiology, Biostatistics, and Data Science.
On the Convergence of Epidemiology, Biostatistics, and Data Science.
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关于流行病学，生物统计学和数据科学的收敛。
DOI：
10.1162/99608f92.9f0215e6
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
McClure LA
中科院分区：
文献类型：
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作者：
Goldstein ND;LeVasseur MT;McClure LA
流行病学、生物统计学和数据科学是包含各种实质性领域的广泛学科。其中的共同点是关注解决复杂问题的定量方法。当实质性领域是卫生和保健时，这种重叠进一步巩固。这些学科的研究人员精通统计学、数据管理和分析、健康和医学，仅举几例。然而，这些领域有一些重要的、也许是相互排斥的属性，需要更紧密的集成。例如，流行病学家在研究设计、测量和因果推理的艺术方面接受了大量的培训。生物统计学家精通方法论技术的理论和应用，以及公共卫生研究的设计和实施。数据科学家在高维数据的计算和可视化方法方面接受同样严格的训练。与数据科学家相比，流行病学家和生物统计学家在计算机科学和信息学方面的专业知识可能较少，而数据科学家可能受益于研究设计和因果推理的工作知识。合作和交叉培训提供了分享和学习这些领域的结构、框架、理论和方法的机会，目的是为解决健康和医疗保健领域的挑战性问题提供新鲜和创新的视角。在本文中，我们首先描述了这些领域的演变，重点关注它们在电子健康数据时代的融合，特别是电子医疗记录（emr）。接下来，我们将介绍协作团队如何设计、分析和实施基于电子病历的研究。最后，我们回顾了主要流行病学、生物统计学和数据科学培训项目的课程，找出差距并为该领域的发展提供建议。
Epidemiology, biostatistics, and data science are broad disciplines that incorporate a variety of substantive areas. Common among them is a focus on quantitative approaches for solving intricate problems. When the substantive area is health and health care, the overlap is further cemented. Researchers in these disciplines are fluent in statistics, data management and analysis, and health and medicine, to name but a few competencies. Yet there are important and perhaps mutually exclusive attributes of these fields that warrant a tighter integration. For example, epidemiologists receive substantial training in the science of study design, measurement, and the art of causal inference. Biostatisticians are well versed in the theory and application of methodological techniques, as well as the design and conduct of public health research. Data scientists receive equivalently rigorous training in computational and visualization approaches for high-dimensional data. Compared to data scientists, epidemiologists and biostatisticians may have less expertise in computer science and informatics, while data scientists may benefit from a working knowledge of study design and causal inference. Collaboration and cross-training offer the opportunity to share and learn of the constructs, frameworks, theories, and methods of these fields with the goal of offering fresh and innovate perspectives for tackling challenging problems in health and health care. In this article, we first describe the evolution of these fields focusing on their convergence in the era of electronic health data, notably electronic medical records (EMRs). Next we present how a collaborative team may design, analyze, and implement an EMR-based study. Finally, we review the curricula at leading epidemiology, biostatistics, and data science training programs, identifying gaps and offering suggestions for the fields moving forward.