Real-time detection of deviations in clinical care in ICU data streams

描述（由申请人提供）：及时检测严重患者状况或相关事件及其缓解仍然是临床实践中的一个重要问题。这在重症患者中尤其如此。为此目的开发的典型的基于计算机的检测方法依赖于临床知识的使用，例如专家导出的规则，这些规则被并入监测和警报系统中。然而，在现有的监测系统中提取和实施这些知识往往是耗时、昂贵和困难的。该提案中的研究工作提供了计算而不是基于专家的解决方案，这些解决方案可以从存储在患者数据存储库中的数据（如电子病历）中构建警报系统。简而言之，我们的方法使用先进的机器学习算法来识别个体患者中不寻常的临床管理模式，相对于与可比患者相关的模式，并发出警报，表明这种差异。我们之前的研究支持此类偏离表明在低于50%的错误警报率下发生临床重要事件，这是非常有希望的。我们建议进一步改进新的方法，并建立一个实时监控和报警系统集成与生产电子病历。我们提出了一个评估系统使用医生的评估警报提出了我们的实时系统重症监护病房（ICU）的病人的情况。项目研究人员组成了一个多学科团队，拥有重症监护医学，计算机科学，生物医学信息学，统计机器学习，基于知识的系统和临床数据存储库的专业知识。

DESCRIPTION (provided by applicant): Timely detection of severe patient conditions or concerning events and their mitigation remains an important problem in clinical practice. This is especially true in the critically ill patient. Typical computer-based detection methods developed for this purpose rely on the use of clinical knowledge, such as expert-derived rules, that are incorporated into monitoring and alerting systems. However, it is often time-consuming, costly, and difficult to extract and implement such knowledge in existing monitoring systems. The research work in this proposal offers computational, rather than expert-based, solutions that build alert systems from data stored in patient data repositories, such as electronic medical records. Briefly, our approach uses advanced machine learning algorithms to identify unusual clinical management patterns in individual patients, relative to patterns associated with comparable patients, and raises an alert signaling this discrepancy. Our previous studies provide support that such deviations indicate clinically important events at false alert rates belo 50%, which is very promising. We propose to further improve the new methodology, and build a real-time monitoring and alerting system integrated with production electronic medical records. We propose an evaluation of the system using physicians' assessment of alerts raised by our real-time system for intensive-care unit (ICU) patient cases. The project investigators comprise a multidisciplinary team with expertise in critical care medicine, computer science, biomedical informatics, statistical machine learning, knowledge based systems, and clinical data repositories.