Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction

By Li-Fang Cheng, Gregory Darnell, Bianca Dumitrascu, Corey J. Chivers, Michael Draugelis, Kai Li, Barbara E. Engelhardt. 2017

36 citations recorded by the source corpus.

In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tests 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 Gaussian process (GP) regression for hospital patient monitoring. We propose MedGP, a statistical framework that incorporates 24 clinical and lab 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. 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 found MedGP improves online prediction over baseline methods for nearly all covariates across different disease subgroups and studies. The publicly available code is at this https URL.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine

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Citations in the corpus

Continual Multi-task Gaussian Processes (2019, methodology): “The most closely related works to ours are Cheng et al. (2017) and Yang et al. (2018).”
Reliable Decision Support using Counterfactual Models (2017, methodology): “As predictive models are becoming more widely used in domains like healthcare where safety is critical (e.g. Li-wei et al. 2015, Schulam and Saria 2015, Alaa et al. 2016, Wiens et al. 2016, Cheng et al. 2017), the framework proposed here is increasingly pertinent.”
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series (2019, methodology): “GPs have also been used for direct forecasting (Cheng et al., 2017).”
An Optimal Policy for Patient Laboratory Tests in Intensive Care Units (2018, unknown): “ght traits are commonly used in computing clinical risk scores or diagnosing sepsis. algorithm to this sparse, irregularly sampled dataset, we adapt the multi-output Gaussian process (MOGP) framework [10] to obtain hourly predictions of patient state with uncertainty quantied, on 17 of the 20 clinic”
What-If Reasoning with Counterfactual Gaussian Processes (2017, background): “…disease trajectory models, for instance, state-of-the-art approaches do not explicitly control for future actions (see e.g., Li-wei et al. 2015, Schulam and Saria 2015, Alaa et al. 2016, Wiens et al. 2016, Cheng et al. 2017), and are therefore sensitive to the treatment policy in the training data.”