Big Data in Public Health: Terminology, Machine Learning, and Privacy.

Big Data in Public Health: Terminology, Machine Learning, and Privacy.
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公共卫生中的大数据:术语,机器学习和隐私。

DOI:
10.1146/annurev-publhealth-040617-014208
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发表时间:
2018-04-01
影响因子:
20.8
通讯作者:
Pejaver V
Pejaver V
中科院分区:
医学1区
文献类型:
--
作者:
Mooney SJ;Pejaver V

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数字世界正在以惊人的速度产生数据,而且还在不断增长。虽然这些“大数据”为了解公共卫生提供了新的机会,但它们仍具有更大的研究和实践潜力。本文探讨了围绕大数据产生的几个关键问题。首先,我们提出了一个大数据源的分类,以澄清术语,并确定在大数据的一些子类型中常见的线程。接下来,我们考虑大数据的常见公共卫生研究和实践用途,包括监测,假设生成研究和因果推理,同时探索机器学习在每种用途中可能发挥的作用。然后,我们考虑大数据革命的伦理影响,特别强调在一个技术正在迅速改变有关隐私需求(甚至意义)的社会规范的世界中,保持适当的隐私关怀。最后,我们提出了关于构建团队和培训的建议,以成功地在研究和实践中使用大数据。
The digital world is generating data at a staggering and still increasing rate. While these ‘Big Data’ have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues arising around big data. First, we propose a taxonomy of sources of big data in order to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.
DOI: 10.1097/ede.0000000000000504
发表时间: 2016-09
期刊: Epidemiology (Cambridge, Mass.)
影响因子: --
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