Author: Yekanth Ram Chalumuri
Title of dissertation: TOWARDS AUTOMATION OF HEMORRHAGE DIAGNOSTICS AND THERAPEUTICS
Date/time: April 10th, 2024 at 12pm
Location: ERF 1207 (IREAP Building)
Committee members:
Dr. Jin-Oh Hahn, Advisor and Chair
Dr. Yang Tao, Dean’s Representative
Dr. Hosam Fathy
Dr. Mark Fuge
Dr. Yancy Diaz-Mercado
Dr. Andrew Reisner
Abstract:
This thesis primarily focuses on developing algorithms and methodologies to achieve the broader aim of advancing the care in hemorrhage diagnostics and therapeutics, especially in low resource settings. The first goal of this thesis is to develop algorithms that can detect hemorrhage using non-invasive physiological signals. Our results showed that our machine learning algorithm can successfully classify various types of hypovolemia. We also developed a physics-based approach to detect and estimate internal hemorrhage even when the body is being compensated by fluid resuscitation using continuous non-invasive hemoglobin measurements. We evaluated the model in silico and determined the maximum acceptable measurement noise that can make these algorithms effective.
The second objective is to advance the regulatory aspects of physiological closed-loop control systems in maintaining blood pressure at a desired value during hemorrhage. Physiological closed-loop control systems offer an exciting opportunity to treat hemorrhage in low resource settings but often face regulatory challenges due to safety concerns. A physics-based model with rigorous validation can improve regulatory aspects of such systems but current validation techniques are very naïve. We developed a physics-based model that can predict blood pressure, heart rate, cardiac output, and hematocrit during hemorrhage and resuscitation and validated using a validation framework that uses sampled digital twins for validation along with more rigorous validation metrics. Overall, the goal of this research is to improve the care and outcomes of patients with hemorrhagic shock by utilizing machine learning, inference, and modeling approaches for early detection and treatment.