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神经重症监护中的大数据与预测分析

Big data and predictive analytics in neurocritical care.

作者信息

Alkhachroum Ayham, Kromm Julie, De Georgia Michael A

机构信息

Miller School of Medicine, Neurocritical Care Division, Department of Neurology, University of Miami, Miami, FL, 33146, USA.

Cumming School of Medicine, Department of Critical Care Medicine, University of Calgary, Calgary, AB, Canada.

出版信息

Curr Neurol Neurosci Rep. 2022 Jan;22(1):19-32. doi: 10.1007/s11910-022-01167-w. Epub 2022 Jan 26.

Abstract

PURPOSE OF REVIEW

To describe predictive data and workflow in the intensive care unit when managing neurologically ill patients.

RECENT FINDINGS

In the era of Big Data in medicine, intensive critical care units are data-rich environments. Neurocritical care adds another layer of data with advanced multimodal monitoring to prevent secondary brain injury from ischemia, tissue hypoxia, and a cascade of ongoing metabolic events. A step closer toward personalized medicine is the application of multimodal monitoring of cerebral hemodynamics, bran oxygenation, brain metabolism, and electrophysiologic indices, all of which have complex and dynamic interactions. These data are acquired and visualized using different tools and monitors facing multiple challenges toward the goal of the optimal decision support system. In this review, we highlight some of the predictive data used to diagnose, treat, and prognosticate the neurologically ill patients. We describe information management in neurocritical care units including data acquisition, wrangling, analysis, and visualization.

摘要

综述目的

描述在管理神经系统疾病患者时重症监护病房中的预测数据和工作流程。

最新发现

在医学大数据时代,重症监护病房是数据丰富的环境。神经重症监护通过先进的多模态监测增加了另一层数据,以防止缺血、组织缺氧和一系列持续的代谢事件导致的继发性脑损伤。向个性化医疗迈进的一步是应用脑血流动力学、脑氧合、脑代谢和电生理指标的多模态监测,所有这些都具有复杂和动态的相互作用。这些数据通过不同的工具和监测器获取并可视化,朝着优化决策支持系统的目标面临多重挑战。在本综述中,我们重点介绍了一些用于诊断、治疗和预测神经系统疾病患者的预测数据。我们描述了神经重症监护病房中的信息管理,包括数据采集、整理、分析和可视化。

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