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

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 covariate

SUBMITTER: Cheng LF 

PROVIDER: S-EPMC7341595 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature