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 [1]. 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 preliminary studies provide support that such deviations often indicate clinically important events for which it is worthwhile to raise an alert. We propose an evaluation based on physician assessment of alerts that are generated from a retrospective set of 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 critical care by sending computer-based reminders and alerts to clinicians. This project uses past patient data, which is stored in electronic form, and machine-learning methods to help develop and refine computer-based alerts to improve healthcare quality and reduce costs.

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
Somers, Scott D
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University of Pittsburgh
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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Luo, Zhipeng; Hauskrecht, Milos (2017) Group-Based Active Learning of Classification Models. Proc Int Fla AI Res Soc Conf 2017:92-97
Xue, Yanbing; Hauskrecht, Milos (2017) Active Learning of Classification Models with Likert-Scale Feedback. Proc SIAM Int Conf Data Min 2017:28-35
Liu, Siqi; Wright, Adam; Sittig, Dean F et al. (2017) Change-Point Detection for Monitoring Clinical Decision Support Systems with a Multi-Process Dynamic Linear Model. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2017:569-572
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
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
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
Hong, Charmgil; Hauskrecht, Milos (2016) Multivariate Conditional Outlier Detection and Its Clinical Application. Proc Conf AAAI Artif Intell 2016:4216-4217
Hauskrecht, Milos; Batal, Iyad; Hong, Charmgil et al. (2016) Outlier-based detection of unusual patient-management actions: An ICU study. J Biomed Inform 64:211-221
Naeini, Mahdi Pakdaman; Cooper, Gregory F (2016) Binary Classifier Calibration Using an Ensemble of Linear Trend Estimation. Proc SIAM Int Conf Data Min 2016:261-269
Liu, Zitao; Hauskrecht, Milos (2016) Learning Linear Dynamical Systems from Multivariate Time Series: A Matrix Factorization Based Framework. Proc SIAM Int Conf Data Min 2016:810-818

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