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

实时检测ICU数据流中临床护理的偏差

基本信息

批准号：
9278178
负责人：
GREGORY F. COOPER
金额：
$ 54.38万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2019-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9278178
关键词：
Algorithms Archives Area Caring Clinical Clinical Data Clinical Management Clinical Trials Complication Computerized Medical Record Computers Critical Care Critical Illness Data Databases Detection Development Environment Evaluation Event Feedback Funding Health Personnel Healthcare Hospitals Immunosuppressive Agents Inpatients Intensive Care Units Knowledge Laboratories Machine Learning Medical Medical Errors Medicine Methodology Methods Modeling Monitor Operative Surgical Procedures Oral Outpatients Patient Care Patients Pattern Performance Pharmaceutical Preparations Physicians Play Practice Management Production Real-Time Systems Records Research Research Personnel Signal Transduction Stream System Tacrolimus Techniques Testing Time United States National Institutes of Health Work base biomedical informatics clinical care clinical data warehouse clinical practice computer science cost data archive design improved individual patient knowledge base learning strategy liver transplantation multidisciplinary prototype public health relevance

项目摘要

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.

描述（由申请人提供）：及时检测严重的患者状况或有关事件及其缓解措施仍然是临床实践中的重要问题。在重症患者中尤其如此。为此目的而开发的典型基于计算机的检测方法取决于将临床知识（例如专家衍生的规则）纳入监视和警报系统的使用。但是，在现有监测系统中提取和实施此类知识通常很耗时，昂贵且昂贵，而且很难。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，这些解决方案是从存储在患者数据存储库中的数据（例如电子医疗记录）中的数据。简而言之，我们的方法使用先进的机器学习算法来识别单个患者的异常临床管理模式，相对于与可比患者相关的模式，并提高了警报，表明了这种差异。我们以前的研究提供了支持，即这种偏差表明临床上重要的事件以错误的警报率50％，这是非常有前途的。我们建议进一步改善新方法，并建立与生产电子病历集成的实时监控和警报系统。我们建议使用医师对我们的实时护理单位（ICU）患者案件提出的警报进行评估对系统进行评估。该项目调查人员组成了一个多学科团队，具有重症监护医学，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。