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Sparse multi-output Gaussian processes for online medical time series prediction

Author(s): Cheng, Li-Fang; Dumitrascu, Bianca; Darnell, Gregory; Chivers, Corey; Draugelis, Michael; et al

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Abstract: Background For real-time monitoring of hospital patients, high-quality inference of patients’ health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on multi-output Gaussian process (GP) regression for hospital patient monitoring. Methods We propose MedGP, a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time. To do this, we develop a highly structured sparse GP kernel to enable tractable computation over tens of thousands of time points while estimating correlations among clinical covariates, patients, and periodicity in patient observations. MedGP has a number of benefits over current methods, including (i) not requiring an alignment of the time series data, (ii) quantifying confidence regions in the predictions, (iii) exploiting a vast and rich database of patients, and (iv) inferring interpretable relationships among clinical covariates. Results We evaluate and compare results from MedGP on the task of online prediction for three patient subgroups from two medical data sets across 8,043 patients. We find MedGP improves online prediction over baseline and state-of-the-art methods for nearly all covariates across different disease subgroups and hospitals. Conclusions The MedGP framework is robust and efficient in estimating the temporal dependencies from sparse and irregularly sampled medical time series data for online prediction. The publicly available code is at
Publication Date: 2020
Citation: Cheng, Li-Fang, Bianca Dumitrascu, Gregory Darnell, Corey Chivers, Michael Draugelis, Kai Li, and Barbara E. Engelhardt. "Sparse multi-output Gaussian processes for online medical time series prediction." BMC Medical Informatics and Decision Making 20, no. 1 (2020). doi:10.1186/s12911-020-1069-4
DOI: 10.1186/s12911-020-1069-4
ISSN: 1472-6947
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: BMC Medical Informatics and Decision Making
Version: Final published version. This is an open access article.

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