Monitoring Big Data During Mechanical Ventilation in the ICU

被引:10
作者
Smallwood, Craig D. [1 ,2 ]
机构
[1] Boston Childrens Hosp, Div Crit Care Anesthesia Crit Care & Pain Med, Boston, MA 02115 USA
[2] Harvard Med Sch, Boston, MA 02115 USA
关键词
big data; data science; machine learning; mechanical ventilation; neural network; RESPIRATORY-DISTRESS-SYNDROME; INTENSIVE-CARE; ARTIFICIAL-INTELLIGENCE; DECISION-SUPPORT; NEURAL-NETWORKS; MORTALITY; SEPSIS; TRIALS; SCORE;
D O I
10.4187/respcare.07500
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
The electronic health record allows the assimilation of large amounts of clinical and laboratory data. Big data describes the analysis of large data sets using computational modeling to reveal patterns, trends, and associations. How can big data be used to predict ventilator discontinuation or impending compromise, and how can it be incorporated into the clinical workflow? This article will serve 2 purposes. First, a general overview is provided for the layperson and introduces key concepts, definitions, best practices, and things to watch out for when reading a paper that incorporates machine learning. Second, recent publications at the intersection of big data, machine learning, and mechanical ventilation are presented.
引用
收藏
页码:894 / 906
页数:13
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