event

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

Media

No media selected

Summary

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.
 

Details

Friday
Jan 3 2025
01:00pm - 03:00pm

Location: Online

In campus calendar: No

Sidebar Content

No sidebar content

Groups

Status

  • Workflow status: Published
  • Created by: Daniela Staiculescu
  • Created: 12/13/2024
  • Modified By: Daniela Staiculescu
  • Modified: 12/13/2024

Related Links

Categories

Keywords

Target Audience