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Autonomous medical care systems are relatively recent developments in biomedical research that aim to leverage the vigilance, precision, and processing power of computers to assist (or replace) humans in providing medical care to patients. Indeed, past research has demonstrated initial promise for autonomous medical care in applications related to anesthesia, hemodynamic management, and diabetes management, to name a few. However, many of these technologies yet do not exhibit the maturity necessary for widespread real-world adoption and regulatory approval. This can be attributed, in part, to several outstanding challenges associated with the design and development of algorithms that interact with physiological processes. Ideally, an autonomous medical care system should be equipped to exhibit (i) transparent behavior, where the system’s perceptions, reasoning, and decisions are human-interpretable; (ii) context-aware behavior, where the system is capable of remaining mindful of contextual and peripheral information in addition to its primary goal; (iii) coordinated behavior, where the system can coordinate multiple actions in synergistic ways to best achieve multiple objectives; (iv) adaptable behavior, where the system is equipped to identify and adapt to variabilities that exist within and across different patients; and (v) uncertainty-aware behavior, where the system can handle imperfect measurements, quantify the uncertainties that arise as a result, and incorporate them into its decisions. As these desires and challenges are specific to autonomous medical care applications and not fully explored in past research in this area, this dissertation presents a sequence of methodologies to model, monitor, and control a physiological process with special emphasis on addressing these challenges. For this purpose, first, a collective variational inference (C-VI) method is presented that facilitates the creation of personalized and generative physiological models from low-information and heterogeneous datasets. The generative physiological model is of special importance for the purposes of this work, as it encodes physiological knowledge by reproducing the patterned randomness that is observed in physiological datasets. Second, a population-informed particle filtering (PIPF) method is presented that fuses the information encoded in the generative model with real-time clinical data to form perceptions of a patient’s states, characteristics, and events. Third, a population-informed variational control (PIVC) method is presented that leverages the generative model, the perceptions of the PIPF algorithm, and user-defined definitions of actions and rewards in order to search for optimal courses of treatment for a patient. These methods together form a physiological decision-support and closed-loop control (PCLC) framework that is intended to facilitate the desirable behaviors sought in the motivations of this work. The performance, merits, and limitations of this framework are analyzed and discussed based on clinically-important case studies on fluid resuscitation for hemodynamic management.