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.
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.
|Hong, Charmgil; Hauskrecht, Milos (2016) Multivariate Conditional Outlier Detection and Its Clinical Application. Proc Conf AAAI Artif Intell 2016:4216-4217|
|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|
|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|
|Liu, Zitao; Hauskrecht, Milos (2016) Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data. Proc Conf AAAI Artif Intell 2016:1273-1279|
|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|
|Liu, Zitao; Hauskrecht, Milos (2015) A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis. Proc Conf AAAI Artif Intell 2015:1798-1804|
|King, Andrew J; Cooper, Gregory F; Hochheiser, Harry et al. (2015) Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System. AMIA Annu Symp Proc 2015:1967-75|
|Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos (2015) Obtaining Well Calibrated Probabilities Using Bayesian Binning. Proc Conf AAAI Artif Intell 2015:2901-2907|
|Liu, Zitao; Hauskrecht, Milos (2015) Clinical time series prediction: Toward a hierarchical dynamical system framework. Artif Intell Med 65:5-18|
|Heim, Eric; Hauskrecht, Milos (2015) Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels. Proceedings (IEEE Int Conf Bioinformatics Biomed) 2015:331-336|
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