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

项目总结/摘要 及时检测严重患者状况或相关事件并缓解其仍然是重要的 临床实践中的问题。这在重症患者中尤其如此。典型的基于计算机的检测 为此目的而开发的方法依赖于临床知识的使用，例如专家得出的规则， 被纳入监控和警报系统。然而，这往往是耗时、昂贵和困难的 在现有的监测系统中提取和应用这些知识。本提案中的研究工作 提供计算而非基于专家的解决方案，根据存储在患者体内的数据构建警报系统， 数据存储库，如电子病历。简单地说，我们的方法使用先进的机器学习 识别个体患者中异常临床管理模式的算法， 与可比较的患者相关，并发出警告信号表示这种差异。我们以前的研究 支持此类偏离表明临床重要事件的误报率低于50%， 很有前途我们建议进一步改进新的方法，并建立一个实时监测和 与生产电子病历集成的警报系统。我们建议对该系统进行评估 使用医生对我们的实时系统为重症监护病房（ICU）患者发出的警报的评估， 例项目研究人员组成了一个多学科团队，具有重症监护医学方面的专业知识， 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。

PROJECT SUMMARY / ABSTRACT 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 below 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.