  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
https://github.com/bee-hive/MedGP.
