Dissertation Defense: Xinlei Chen

"Machine Learning in Sepsis Clinical Management" Department of Biostatistic and Health Data Science, School of Public Health. Advisors and Committee Chairs Lu TangVictor TalisaAbstract Sepsis is a serious and potentially life-threatening condition caused by the body's response to an infection. It affects roughly two million individuals in the US each year. Despite decades of research, it remains a leading cause of mortality in critical care. One of the biggest challenges in sepsis management is heterogeneity. Patients can look very different from one another – in terms of their underlying conditions, disease progression, and response to treatment. Because of this variability, clinical decision-making is often difficult. This dissertation focuses on addressing challenges posed by patient heterogeneity in sepsis care using machine learning methods. In the first project, we develop Federated Learning of Robust Individualized Decision Rules (FLoRI), a flexible machine learning-based individualized treatment framework that can be deployed in complex clinical settings. Through a novel objective function and federated learning, FLoRI provides safe treatment recommendations, improves generalizability across patient populations, and mitigates infrastructure limitations across healthcare systems simultaneously. The performance of FLoRI is demonstrated through an application to University of Pittsburgh Medical Center (UPMC) sepsis patients. FLoRI effectively improves the survival by 2-3 percentage points among sepsis patients and by 10 percentage points among sepsis patients with higher risk of death. In the second project, we develop Tilted Individualized Decision Rules (TIDE), an individualized treatment framework that is robust to data contamination. Through tilted empirical risk minimization, TIDE down-weights corrupted observations and emphasizes the signal from the majority of the data, leading to more reliable treatment recommendations. TIDE is then extended to TI