678864 event 1734128596 1734128663 <![CDATA[Ph.D. Dissertation Defense - Anni Zhou]]> Title:  Trustworthy and Robust Early Sepsis Prediction for Intensive Care Unit Patients using Reinforcement Learning and Conformal Prediction

Committee:

Dr. Raheem Beyah, ECE, Chair, Advisor

Dr. Rishikesan Kamaleswaran, Duke, Co-Advisor

Dr. Yao Xie, ISyE

Dr. Saman Zonouz, ECE

Dr. Omer Inan, ECE

Dr. David Anderson, ECE

]]> This dissertation develops advanced machine learning frameworks to improve early sepsis prediction in ICU patients. Three novel approaches are introduced: OnAI-Comp, a multi-armed bandit framework that selects the best-performing model for each patient; Sepsyn-OLCP, a reinforcement learning algorithm with conformal prediction for reliable outcomes; and NeuroSep-CP-LCB, a neural network-based contextual bandit integrating conformal prediction for calibrated, data-driven decisions. These methods prioritize accuracy and trustworthiness, addressing critical needs in predictive healthcare and advancing sepsis prediction in critical care environments.
 

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