Graduation Term

Summer 2025

Degree Name

Master of Science (MS)

Department

Department of Mathematics

Committee Chair

Xiaotian Dai

Committee Co-Chair

Maochao Xu

Committee Member

Papa Sissokho

Abstract

Unplanned hospital readmissions represent a significant challenge for healthcare systems, contributing to substantial financial burdens and highlighting gaps in patient care coordination. In the U.S., approximately 20% of Medicare beneficiaries are readmitted within 30 days, costing billions annually. Social determinants of health, such as income, housing stability, and social support, account for up to 80% of health outcomes, yet their integration into predictive models remains underexplored. This study introduces a novel Bayesian framework for predicting 30-day readmission risk, combining Gaussian Process models with spike-and-slab priors and Bayesian Lasso regression with Laplace priors. Utilizing Markov Chain Monte Carlo methods, the approach captures nonlinear relationships and performs interpretable variable selection, emphasizing both clinical and social determinants of health factors. Applied to simulated and real-world data from OSF Healthcare, the Bayesian Lasso model achieved an area under the Receiver Operating Characteristic curve of 0.88, identifying key predictors like housing instability and prior emergency department visits, while the Gaussian Process model (area under the curve = 0.752) highlighted clinical malnutrition as a significant risk factor. This dual approach enhances risk stratification, supports targeted interventions, and informs equitable healthcare policies, advancing value- based care and reducing readmissions.

Access Type

Thesis-Open Access

DOI

https://doi.org/10.30707/ETD.1763755358.936349

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