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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