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

描述（由申请人提供）：在临床实践中，及时发现严重患者病情或相关事件并减轻其影响仍然是一个重要问题。对于危重病人尤其如此。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，例如专家衍生的规则，这些规则被纳入监测和警报系统。然而，在现有的监测系统中提取和实施这些知识通常是耗时、昂贵和困难的。本提案中的研究工作提供了计算解决方案，而不是基于专家的解决方案，这些解决方案根据存储在患者数据存储库（如电子病历）中的数据构建警报系统。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，并提出警告，表明这种差异。我们之前的研究提供了支持，这些偏差在错误警报率低于50%的情况下表明临床重要事件，这是非常有希望的。我们建议进一步改进新方法，构建与生产电子病历相结合的实时监测预警系统。我们建议使用医生对我们的重症监护病房（ICU）患者病例实时系统提出的警报进行评估。项目研究人员由一个多学科团队组成，他们在重症监护医学、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方面具有专业知识。

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.