Machine Learning and Decision Support in Critical Care.
Machine Learning and Decision Support in Critical Care.
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DOI：
10.1109/jproc.2015.2501978
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发表时间：
2016-02
期刊：
影响因子：
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通讯作者：
Clifford GD
中科院分区：
文献类型：
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作者：
Johnson AE;Ghassemi MM;Nemati S;Niehaus KE;Clifton DA;Clifford GD
临床数据管理系统通常为护理人员团队提供有用的信息，这些信息来自经常动态变化的大型数据源，有时是高度异构的数据源。在过去的十年中，人们对使用这些数据源的兴趣显著增加，从简单地重复使用标准临床数据库进行事件预测或决策支持，到将动态和患者特定信息纳入临床监测和预测问题。然而，在大多数情况下，商业临床数据库的设计是为了记录临床活动，用于报告、责任和计费，而不是用于开发新的算法。随着人们对“医疗记录的二次利用”和“大数据”分析越来越感兴趣，了解当前数据库的局限性以及需要改变什么才能进入“精准医疗”时代是很重要的。这篇评论文章涵盖了许多涉及的问题，收集和预处理的重症监护数据。在重症监护的三个挑战被认为是：划分，腐败和复杂性。一系列的应用程序解决这些问题，包括现代化的静态敏锐度评分;在线患者跟踪;个性化的预测和风险评估;伪影检测;状态估计;并纳入多模式数据源，如基因组和自由文本数据。
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding “secondary use of medical records” and “Big Data” analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of “precision medicine.” This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; on-line patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.