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：
8641014
负责人：
GREGORY F. COOPER
金额：
$ 58.02万
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 2018-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8641014
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 Decision Making Detection Development Electronics Environment Evaluation Event Feedback Funding Health Personnel Healthcare Hospitals Immunosuppressive Agents Individual Information Systems 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 Relative (related person)Research Research Personnel Signal Transduction Solutions Stream System Tacrolimus Techniques Testing Time United States National Institutes of Health Work base biomedical informatics clinical care clinical practice computer science design improved knowledge base liver transplantation multidisciplinary prototype public health relevance

项目摘要

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.

项目摘要 /摘要及时检测严重的患者状况或有关事件及其缓解措施仍然很重要临床实践中的问题。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开发的方法取决于临床知识的使用，例如专家衍生的规则，被整合到监视和警报系统中。但是，这通常很耗时，昂贵且困难在现有监视系统中提取和实施此类知识。该提案中的研究工作提供计算的解决方案，而不是基于专家的解决方案，这些解决方案从存储在患者中的数据中构建警报系统数据存储库，例如电子病历。简而言之，我们的方法使用高级机器学习相对于模式，识别个别患者的异常临床管理模式的算法与可比的患者相关，并提高警报，表明这种差异。我们以前的研究提供此类偏差表明以低于50％的错误警报率表示临床上重要事件的支持，即非常有前途。我们建议进一步改善新方法，并建立实时监控和与生产电子病历集成的警报系统。我们提出对系统的评估使用医师对我们的重症监护病人（ICU）患者实时系统提出的警报的评估案例。该项目调查人员组成了一个具有重症监护医学专业知识的多学科团队，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。