The artificial pancreas (AP) is a set of increasingly sophisticated devices and algorithms that will automate the delivery of insulin to patients with type-1 diabetes. While the AP concept promises to alleviate the burden posed by the self-management of blood glucose levels, it also poses significant risks arising from a combination of external disturbances such as patient meals, physical activity, sensor errors, network delays and physiological variations.

Performance monitoring data (e.g., measurements, logs, events) are becoming increasingly accessible and abundant (in terms of cost and availability) in modern distributed complex systems such as computer systems and networks, integrated buildings, industrial systems, transportation networks and power-grids. With efficient exploration of such data, health monitoring, diagnosis and prognosis can be greatly improved from the current state-of-the-art.

The project is to develop an Executable Medical Best Practice Guidance (EMBG) system to assist the adherence of acute patient care at a rural hospital to the best medical practice as if the patient care is at a regional hospital. The EMBG system is adaptive to the changing needs of a patient and physical resource availability, just as a GPS-enabled navigation system can driver’s preference and accommodate road condition changes.

This project is a component of a larger effort is to develop the foundations of modeling, synthesis and development of verified medical device software and systems from verified closed-loop models of the device and organ(s). This research spans both implantable medical devices such as cardiac pacemakers and physiological control systems such as drug infusion pumps which have multiple networked medical systems. Here we focus on advancing two aspects of this work: (1) development of patient-specific models and therapies and (2) multi-scale modeling of complex physiological phenomena.

The CyberCardia project will lead to significant advances in the state of the art for system verification and cardiac therapies based on the use of formal methods and closed-loop control and verification.  The animating vision for the work is to enable the development of a true in silico design methodology for medical devices that can be used to speed the development of new devices and to provide greater assurance that their behavior matches designer intentions, and to pass regulatory muster more quickly so that they can be used on patients needing their care.  

This project advances the scientific knowledge on design methods for improving the resilience of civil infrastructures to disruptions. To improve resilience, critical services in civil infrastructure sectors must utilize new diagnostic tools and control algorithms that ensure survivability in the presence of both security attacks and random faults, and also include the models of incentives of human decision makers in the design process.

The presentation materials cover results obtained for the two above-mentioned projects.  The poster presents material on an algebraic approach to modeling systems with both continuous and discrete behavior.  The framework is based on process algebra, which was developed for discrete systems, and features the development of a tree-based semantic model, called generalized synchronization trees, that uniformly captures a very general notions of time.

Starting from a database of 100’s of real patient electrogram records, we describe how to develop and use a large in-silico cohort consisting of 10,000+ heart models to improve the planning and execution of a clinical trial (CT) for implantable cardioverter defibrillators (ICDs). We illustrate our approach by applying it retrospectively to a real CT that compares two discrimination algorithms (DA) within ICDs for the detection of potentially fatal cardiac arrhythmias. The CT posited that one algorithm would be better than the other but the results of the trial