The critical care environment drives huge volumes of data, and clinicians are tasked with quickly processing this data and responding to it urgently. The neurocritical care environment increasingly involves EEG, multimodal intracranial monitoring, and complex imaging which preclude comprehensive human synthesis, and requires new concepts to integrate data into clinical care. By definition, Big Data is data that cannot be handled using traditional infrastructures and is characterized by the volume, variety, velocity, and variability of the data being produced. Big Data in the neurocritical care unit requires rethinking of data storage infrastructures and the development of tools and analytics to drive advancements in the field. Preprocessing, feature extraction, statistical inference, and analytic tools are required in order to achieve the primary goals of Big Data for clinical use: description, prediction, and prescription. Barriers to its use at bedside include a lack of infrastructure development within the healthcare industry, lack of standardization of data inputs, and ultimately existential and scientific concerns about the outputs that result from the use of tools such as artificial intelligence. However, as implied by the fundamental theorem of biomedical informatics, physicians remain central to the development and utility of Big Data to improve patient care.

中文翻译：

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神经重症监护：从实验室到床边（Claude Hemphill、Michael James 编）在神经重症监护中集成和使用大数据。


重症监护环境产生大量数据，临床医生的任务是快速处理这些数据并紧急做出响应。神经重症监护环境越来越多地涉及脑电图、多模式颅内监测和复杂成像，这些都妨碍了人类的全面综合，并且需要新概念将数据整合到临床护理中。根据定义，大数据是无法使用传统基础设施处理的数据，其特点是所产生的数据的数量、种类、速度和可变性。神经重症监护病房中的大数据需要重新思考数据存储基础设施以及工具和分析的开发，以推动该领域的进步。为了实现临床使用大数据的主要目标：描述、预测和处方，需要预处理、特征提取、统计推断和分析工具。其在床边使用的障碍包括医疗保健行业内缺乏基础设施开发、数据输入缺乏标准化，以及最终对使用人工智能等工具产生的输出的存在和科学担忧。然而，正如生物医学信息学基本定理所暗示的那样，医生仍然是大数据的开发和利用以改善患者护理的核心。