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.

Public Health Relevance

There remain numerous opportunities to reduce medical errors in the intensive care unit (ICU). This project develops and evaluates a new clinical monitoring and alerting framework that uses electronic medical records and machine-learning methods to send alerts concerning clinical decisions in the ICU that are unexpected given the clinical context and may represent medical errors.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Marcus, Stephen
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University of Pittsburgh
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Naeini, Mahdi Pakdaman; Cooper, Gregory F (2018) Binary Classifier Calibration Using an Ensemble of Piecewise Linear Regression Models. Knowl Inf Syst 54:151-170
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Liu, Siqi; Wright, Adam; Hauskrecht, Milos (2017) Online Conditional Outlier Detection in Nonstationary Time Series. Proc Int Fla AI Res Soc Conf 2017:86-91
Liu, Zitao; Hauskrecht, Milos (2017) A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection. Proc ACM Int Conf Inf Knowl Manag 2017:1169-1177
Liu, Siqi; Wright, Adam; Hauskrecht, Milos (2017) Change-Point Detection Method for Clinical Decision Support System Rule Monitoring. Artif Intell Med (2017) 10259:126-135
Xue, Yanbing; Hauskrecht, Milos (2017) Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking. Proc Int Fla AI Res Soc Conf 2017:164-169
Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy et al. (2016) An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data. Knowl Inf Syst 46:115-150

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