Trustworthy and Robust Early Sepsis Prediction for Intensive Care Unit Patients using Reinforcement Learning and Conformal Prediction
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Author(s)
Zhou, Anni
Advisor(s)
Kamaleswaran, Rishikesan
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Abstract
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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Date
2025-01-08
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Text
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Dissertation