Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China.
Eur J Med Res. 2024 Mar 25;29(1):201. doi: 10.1186/s40001-024-01764-0.
Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.
大数据技术自云计算时代以来已经迅速发展。传统的数据存储、提取、转换和分析技术已经不适合医疗策略中大数据的大容量、多样性、高速处理和低价值密度，因此需要开发新的大数据应用技术。在这方面，我们研究了麻醉学中最近的大数据平台突破，并设计了一个基于云系统的麻醉决策模型，用于存储和分析来自麻醉记录的大量数据。所提出的麻醉决策分析平台通过几个编程工具对医疗记录进行分布式计算，并提供关键字搜索、数据过滤和基本统计等服务，以减少决策者的不准确和主观判断。重要的是，它有可能改善麻醉策略并制定个性化的麻醉决策，降低围手术期并发症的可能性。
