Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland; and the Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan.
Obstet Gynecol. 2017 Feb;129(2):249-264. doi: 10.1097/AOG.0000000000001865.
Technical advances in science have had broad implications in reproductive and women's health care. Recent innovations in population-level data collection and storage have made available an unprecedented amount of data for analysis while computational technology has evolved to permit processing of data previously thought too dense to study. "Big data" is a term used to describe data that are a combination of dramatically greater volume, complexity, and scale. The number of variables in typical big data research can readily be in the thousands, challenging the limits of traditional research methodologies. Regardless of what it is called, advanced data methods, predictive analytics, or big data, this unprecedented revolution in scientific exploration has the potential to dramatically assist research in obstetrics and gynecology broadly across subject matter. Before implementation of big data research methodologies, however, potential researchers and reviewers should be aware of strengths, strategies, study design methods, and potential pitfalls. Examination of big data research examples contained in this article provides insight into the potential and the limitations of this data science revolution and practical pathways for its useful implementation.
科学技术的进步在生殖和妇女保健方面产生了广泛的影响。最近在人口水平的数据收集和存储方面的创新为分析提供了前所未有的大量数据，而计算技术的发展也使得以前认为过于密集而无法研究的数据得以处理。“大数据”一词用于描述数据量、复杂性和规模都急剧增加的情况。在典型的大数据研究中，变量的数量很容易达到数千个，这挑战了传统研究方法的极限。无论它被称为什么，先进的数据方法、预测分析或大数据，这种在科学探索方面前所未有的革命都有可能极大地帮助妇产科领域的广泛研究。然而，在实施大数据研究方法之前，潜在的研究人员和审查人员应该了解其优势、策略、研究设计方法和潜在的陷阱。本文中包含的大数据研究示例的研究提供了对这一数据科学革命的潜力和局限性的深入了解，以及其有效实施的实用途径。
