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首页> 外文期刊>Pediatric critical care medicine: a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies >Toward unreasonable effectiveness of cardiac ICU data: Artificial intelligence in pediatric cardiac intensive care
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Toward unreasonable effectiveness of cardiac ICU data: Artificial intelligence in pediatric cardiac intensive care

机译:走向心脏ICU数据的不合理有效性:小儿心脏重症监护中的人工智能

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The title of this editorial resonates with the seminal article on "unreasonable effectiveness of data" (as proposed by the Google chief scientist Peter Norvig) that elaborates on how elegant models can make large volumes of data powerful, similar to how physics can be explained by simple mathematical equations (1, 2). However, creating the models for large volumes of data to be effective is not always easy, and it has been quoted by several authorities in healthcare data circles that in clinical medicine (including critical care), we are "dying of thirst for information in an ocean of data." The potential of a new era in medicine is dawning upon us, with the advent of applications of data analytical methodologies and artificial intelligence techniques in clinical medicine, or "medical intelligence." In spite of the exponential rise in computing power and storage capability as well as emergence of "big data" in healthcare, there has been a paucity of reports on artificial intelligence methodologies in the ICU setting since an early review of this topic (3).
机译:这篇社论的标题与关于“数据的不合理有效性”(由Google首席科学家Peter Norvig提出)的开创性文章产生共鸣,该文章详细阐述了优雅的模型如何使大量数据变得强大,类似于物理学如何解释简单的数学方程式(1、2)。但是,要创建有效的大量数据模型并不总是那么容易,而且医疗数据领域的一些权威机构都引用这种说法,即在临床医学(包括重症监护)中,我们“渴望获得医学信息。数据的海洋。”随着数据分析方法学和人工智能技术在临床医学(或“医学智能”)中的应用的出现,医学新时代的潜力正在临到我们。尽管计算能力和存储能力呈指数级增长,并且在医疗保健领域出现了“大数据”,但自从对此主题进行早期审查以来,ICU环境中关于人工智能方法学的报道却很少(3)。

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