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 belo 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.
| Naeini, Mahdi Pakdaman; Cooper, Gregory F (2018) Binary Classifier Calibration Using an Ensemble of Piecewise Linear Regression Models. Knowl Inf Syst 54:151-170 |
| Luo, Zhipeng; Hauskrecht, Milos (2018) Hierarchical Active Learning with Group Proportion Feedback. IJCAI (U S) 2018:2532-2538 |
| 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, 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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