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Biomedical Data: From Bayesian Changepoint Detection to Machine Learning Applications  \nPermalink  \n[https://escholarship.org/uc/item/2wm62504](https://escholarship.org/uc/item/2wm62504)  \nISBN  \n9798244848441  \nAuthor  \nLiu, Vincent  \nPublication Date  \n2026-05-12  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nStatistical Methods for Complex Biomedical Data: From Bayesian Changepoint Detection to Machine Learning Applications  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Statistics  \nby  \nVincent Bojie Liu  \n2026  \n© Copyright by Vincent Bojie Liu  \n2026  \nABSTRACT OF THE DISSERTATION  \nStatistical Methods for Complex Biomedical Data: From Bayesian Changepoint Detection to Machine Learning Applications  \nby  \nVincent Bojie Liu  \nDoctor of Philosophy in Statistics  \nUniversity of California, Los Angeles, 2026  \nProfessor Yingnian Wu, Chair  \nThis dissertation develops and applies advanced statistical and machine learning methods to address challenges in biomedical data analysis. The work spans six interconnected studies that contribute methodological innovations and practical applications across epidemiological surveillance, longitudinal modeling, clinical prediction, and precision medicine.  \nChapter 1 presents efficient MCMC algorithms for Bayesian changepoint detection in overdispersed autoregressive count data, with applications to COVID-19 surveillance. Chapter 2 introduces penalized fractional polynomials via mixed models for flexible non-linear longitudinal data analysis. Chapter 3 compares machine learning models for hospital readmission prediction. Chapter 4 optimizes blood glucose predictions using advanced forecasting techniques. Chapter 5 develops a generative latent protocol model for sepsis treatment in ICU settings. Chapter 6 presents an explainable machine learning model for hypertension screening in Type 1 diabetes patients using continuous glucose monitoring data.  \nTogether, these studies demonstrate the power of combining classical statistical theory with modern computational methods to solve pressing problems in biomedical research and clinical practice.  \nThe dissertation of Vincent Bojie Liu is approved.  \nQing Zhou  \nOscar Hernan Madrid Padilla  \nYuhua Zhu  \nYingnian Wu, Committee Chair  \nUniversity of California, Los Angeles  \n2026  \nContents  \nAcknowledgments xviii  \nVita xix  \n1 Introduction 1  \n2 Efficient MCMC Algorithms for Bayesian Changepoint Detection in  \nOverdispersed Autoregressive Count Data 3  \n2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4  \n2.2.1 Motivation and Problem Statement . . . . . . . . . . . . . . . 4  \n2.2.2 Motivating Example: COVID-19 Surveillance . . . . . . . . . 5  \n2.2.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2.2.4 Our Contributions . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.2.5 Paper Organization . . . . . . . . . . . . . . . . . . . . . . . . 8  \n2.3 Statistical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \n2.3.1 Model Specification . . . . . . . . . . . . . . . . . . . . . . . . 8  \n2.3.2 Prior Distributions . . . . . . . . . . . . . . . . . . . . . . . . 9  \n2.3.3 Identifiability Considerations .................. 10  \n2.4 MCMC Algorithm ............................ 10  \n2.4.1 Metropolis-within-Gibbs Sampler ................ 10  \n2.4.2 Regression Coefficient Updates . . . . . . . . . . . . . . . . . 11  \n2.4.3 Dispersion Parameter Updates . . . . . . . . . . . . . . . . . 11  \n2.4.4 Hyperparameter Updates (Gibbs) ................ 12  \n2.4.5 Changepoint Update ....................... 12  \n2.4.6 Compu","cbCaieMzfNKOa9xZ","https://ap.wps.com/l/cbCaieMzfNKOa9xZ","pdf",10496831,158,"English","# Introduction\n## Chapter 1: Efficient MCMC Algorithms for Bayesian Changepoint Detection in Overdispersed Autoregressive Count Data\n## Chapter 2: Penalized Fractional Polynomials via Mixed Models for Flexible Non-Linear Longitudinal Data Analysis\n## Chapter 3: Machine Learning Models for Hospital Readmission Prediction\n## Chapter 4: Advanced Forecasting for Blood Glucose Predictions\n## Chapter 5: Generative Latent Protocol Model for Sepsis Treatment in ICU Settings\n## Chapter 6: Explainable Machine Learning for Hypertension Screening in Type 1 Diabetes","[{\"question\":\"What is the main focus of this dissertation?\",\"answer\":\"It develops and applies advanced statistical and machine learning methods for complex biomedical data, spanning surveillance, longitudinal modeling, clinical prediction, and precision medicine.\"},{\"question\":\"What does Chapter 1 address?\",\"answer\":\"It presents efficient MCMC algorithms for Bayesian changepoint detection in overdispersed autoregressive count data, demonstrated with COVID-19 surveillance.\"},{\"question\":\"How does the dissertation handle clinical prediction and screening tasks?\",\"answer\":\"It includes machine learning model comparisons for hospital readmission prediction, advanced forecasting for blood glucose, a generative model for ICU sepsis treatment, and an explainable model for hypertension screening using continuous glucose monitoring data.\"}]","Statistical Methods for Complex Biomedical Data: From Bayesian Changepoint Detection to Machine Learning Applications | PDF",398]