Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories

Abstract excerpt

Gaussian processes (GPs) are a versatile nonparametric model for nonlinear regression and have been widely used to study spatiotemporal phenomena. However, standard GPs offer limited interpretability and generalizability for datasets with naturally occurring hierarchies. With large-scale, rapidly-updating electronic health record (EHR) data, we want to study patient trajectories across diverse patient cohorts whil...

Topics

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• Algorithms
• Bayes Theorem
• Electronic Health Records
• Markov Chains
• Medical Informatics
• Phenotype
• Pulmonary Disease, Chronic Obstructive
• Supervised Machine Learning

Identifiers and source

Literature Corpus work

f57e0a3b-a2a4-5e82-8c0f-d66e43c7e88a

DOI

10.1101/2021.10.01.462821