Data-driven ICU management: Using Big Data and algorithms to improve outcomes
Data-driven ICU management: Using Big Data and algorithms to improve outcomes
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DOI：
10.1016/j.jcrc.2020.09.002
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发表时间：
2020-12-01
影响因子：
3.7
通讯作者：
Meyfroidt, Geert
中科院分区：
文献类型：
--
作者：
Carra, Giorgia;Salluh, Jorge I. F.;Meyfroidt, Geert
重症监护病房 (ICU) 的数字化导致床边收集的临床数据越来越多。 “大数据”一词可用于指对收集大量不同来源和格式的数据的数据集进行分析。复杂性和多样性定义了大数据的价值。事实上，对这些数据集的回顾性分析可以产生新的知识，从而潜在地改进临床实践。尽管大数据分析在医学研究中取得了良好的开端，同行评审的文章数量不断增加，但在 ICU 临床实践中的应用非常有限。未来应该努力验证从临床大数据中提取的知识并将其应用到临床中。在本文中，我们介绍了 ICU 中的大数据，从数据收集和数据分析，到基于 ICU 数据的预后、预测和分类模型的主要成功示例。此外，我们还重点关注这些模型在到达床边并有效改善 ICU 护理所面临的主要挑战。 (C) 2020 由爱思唯尔公司出版
The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data being collected at the bedside. The term "Big Data" can be used to refer to the analysis of these datasets that collect enormous amount of data of different origin and format. Complexity and variety define the value of Big Data. In fact, the retrospective analysis of these datasets allows to generate new knowledge, with consequent potential improvements in the clinical practice. Despite the promising start of Big Data analysis in medical research, which has seen a rising number of peer-reviewed articles, very limited applications have been used in ICU clinical practice. A close future effort should be done to validate the knowledge extracted from clinical Big Data and implement it in the clinic.In this article, we provide an introduction to Big Data in the ICU, from data collection and data analysis, to the main successful examples of prognostic, predictive and classification models based on ICU data. In addition, we focus on the main challenges that these models face to reach the bedside and effectively improve ICU care. (C) 2020 Published by Elsevier Inc.