The main aim of the thesis is to advance the technology in the development ofalgorithms and methodologies that will advance the care in hemorrhage diagnostics and therapeutics in low resource settings. The first objective of this thesis is to develop algorithms to primarily detect internal hemorrhage using non-invasive multi-modal physiological sensing. We developed a machine learning algorithm that can classify various types of hypovolemia and is shown to be performing superior to the algorithms developed primarily based on vital signs. To address the limitations in the data-driven approaches, we explored physics-based approaches to detect internal hemorrhage. In silico analysis showed that our physics-based algorithms can not only detect hemorrhage but also can detect hemorrhage even when hemorrhage is being compensated by fluid resuscitation.

The second objective is to advance the regulatory aspects of physiological closed-loopcontrol systems in maintaining blood pressure at a desired value during hemorrhage and resuscitation. 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 naive. We developed a physics-based model that can predict hemodynamics during hemorrhage and resuscitation and validated these factors using a validation framework that uses sampled digital twins. Then we utilized the validated model to evaluate its efficacy in predicting the performance capability of the model and virtual patient generator in predicting the closed-loop controller metrics of unseen experimental data.

To summarize, we tried to improve the hemorrhage care using novel algorithmdevelopment and in silico validation and evaluation of computation models that can be used to treat hemorrhage.