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

项目摘要/摘要 及时发现严重的患者病情或相关事件及其缓解仍然是重要的 临床实践中存在的问题。这种情况在危重病人身上尤其明显。典型的基于计算机的检测 为此目的开发的方法依赖于临床知识的使用，例如专家推导的规则，这些规则 整合到监控和警报系统中。然而，这通常是耗时、昂贵和困难的 在现有的监测系统中提取和实施此类知识。这项提案中的研究工作 提供计算型解决方案，而不是基于专家的解决方案，根据存储在患者中的数据构建警报系统 数据存储库，如电子病历。简而言之，我们的方法使用高级机器学习 与模式相关的识别个体患者异常临床管理模式的算法 与可比患者相关，并发出警报，发出这种差异的信号。我们之前的研究 在错误警报率低于50%的情况下，支持此类偏差指示临床重要事件，即 很有希望。我们建议进一步完善新的方法，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 使用医生对我们的重症监护病房(ICU)患者实时系统发出的警报的评估 案子。项目调查人员由一个拥有重症监护医学专业知识的多学科团队组成， 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 数据仓库。

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.