Smallwood Craig D
Division of Critical Care of Anesthesia, Critical Care and Pain Medicine, Boston Children's Hospital.
Harvard Medical School, Boston, Massachusetts.
Respir Care. 2020 Jun;65(6):894-910. doi: 10.4187/respcare.07500.
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.
电子健康记录能够整合大量临床和实验室数据。大数据指的是利用计算模型对大型数据集进行分析，以揭示模式、趋势和关联。如何利用大数据来预测撤机或即将出现的通气功能障碍，以及如何将其纳入临床工作流程？本文有两个目的。其一，为非专业人士提供一个总体概述，并介绍关键概念、定义、最佳实践以及阅读包含机器学习内容的论文时需要注意的事项。其二，展示大数据、机器学习与机械通气交叉领域的近期出版物。
