Collaborative Research: CPS: Medium: Automating Complex Therapeutic Loops with Conflicts in Medical Cyber-Physical Systems

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Critically ill patients receive multiple treatments, which can be automated for clinicians to enable focus on high-level decision-making tasks, while reducing effort on tedious monitoring and titration tasks. However, automation of multiple critical care treatments presents unique challenges. Each treatment given to a patient elicits multiple physiological changes. Thus, when multiple isolated automated treatments are given to a patient, they can conflict and drive the patient to unsafe physiological states. Regardless, most previous work has neglected such conflicts. To address this challenge, we will develop innovative mathematical algorithms to enable safe automated patient monitoring, treatment guidance, and reconciliation of potentially conflicting medical treatments. We will achieve this goal by leveraging and advancing theory of control, inference, and optimization, thereby creating new knowledge in these fields. Successful completion of this project will lead to new approaches to the coordination and mediation of complex interacting automation systems, including healthcare and medical systems.This project brings together the (cyber) disciplines of data science and control theory, in support of safe and conflict-aware automated treatments in the (physical) discipline of patient therapeutics. We propose to develop a transformative universal framework that can mediate conflicts and assure patient safety in the automation of complex critical care loops in medical CPS. We will develop (i) physiological monitoring enabled by sequential filtering to track the safety of a patient’s internal state using limited measurements; (ii) mediation of complex therapeutic loops enabled by multivariable nonlinear output-feedback control augmented by barrier certificates; and (iii) dynamic updating of treatment setpoints enabled by population-informed adaptation to determine safely reachable patient-specific treatment setpoints. We will demonstrate the efficacy of the proposed framework using a complex critical care resuscitation application as a compelling case study. The success of this project will lead to a suite of innovative technologies deployable to various medical care settings, which can drastically advance autonomy in next-generation medical CPS.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.