Data Science in Environmental Health Research

被引:9
|
作者
Choirat, Christine [1 ,2 ]
Braun, Danielle [3 ,4 ]
Kioumourtzoglou, Marianthi-Anna [5 ]
机构
[1] Swiss Fed Inst Technol, Swiss Data Sci Ctr, Zurich, Switzerland
[2] EPFL, Zurich, Switzerland
[3] Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA USA
[4] Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02115 USA
[5] Columbia Univ, Mailman Sch Publ Hlth, Dept Environm Hlth Sci, New York, NY 10027 USA
基金
美国国家环境保护局;
关键词
Data science; Big data; Environmental health research; Reproducibility; Environmental mixtures; High-dimensional; Research data platforms; SATELLITE-DERIVED PM2.5; AIR-POLLUTION; SELECTION; MODEL; EXPOSURE; INFERENCE; MIXTURES; IMPACTS;
D O I
10.1007/s40471-019-00205-5
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Purpose of ReviewData science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper, we review how data science can help advance environmental health research.Recent FindingsWe discuss the concepts of computationally scalable handling of big data and the design of efficient research data platforms and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures and prediction models. Finally, we discuss tools for reproducible research.SummaryIn this paper, we present opportunities to improve environmental research capabilities by embracing data science and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.
引用
收藏
页码:291 / 299
页数:9
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