TY - JOUR
T1 - Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories
AU - Cui, Sunny
AU - Yoo, Elizabeth C.
AU - Li, Didong
AU - Laudanski, Krzysztof
AU - Engelhardt, Barbara E.
PY - 2022
Y1 - 2022
N2 - 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 while preserving patient subgroup structure. In this work, we partition our cohort of over 2000 COVID-19 patients by sex and ethnicity. We develop and apply a hierarchical Gaussian process and a mixture of experts (MOE) hierarchical GP model to fit patient trajectories on clinical markers of disease progression. A case study for albumin, an effective predictor of COVID-19 patient outcomes, highlights the predictive performance of these models. These hierarchical spatiotemporal models of EHR data bring us a step closer toward our goal of building flexible approaches to capture patient data that can be used in real-time systems*.
AB - 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 while preserving patient subgroup structure. In this work, we partition our cohort of over 2000 COVID-19 patients by sex and ethnicity. We develop and apply a hierarchical Gaussian process and a mixture of experts (MOE) hierarchical GP model to fit patient trajectories on clinical markers of disease progression. A case study for albumin, an effective predictor of COVID-19 patient outcomes, highlights the predictive performance of these models. These hierarchical spatiotemporal models of EHR data bring us a step closer toward our goal of building flexible approaches to capture patient data that can be used in real-time systems*.
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M3 - Article
C2 - 34890155
AN - SCOPUS:85122528379
SN - 2335-6936
VL - 27
SP - 266
EP - 277
JO - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
ER -