A Data-Driven Approach to Artificial Pancreas Verification and Synthesis

This paper presents a case study of a data driven approach to verification and parameter synthesis for artificial pancreas control systems which deliver insulin to patients with type-1 diabetes (T1D). We present a new approach to tuning parameters using non-deterministic data-driven models for human insulin-glucose regulation, which are inferred from patient data using multiple time scales. Taking these equations as constraints, we model the behavior of the entire closed loop system over a five-hour time horizon cast as an optimization problem. Next, we demonstrate this approach using patient data gathered from a previously conducted outpatient clinical study involving insulin and glucose data collected from 50 patients with T1D and 40 nights per patient. We use the resulting data-driven models to predict how the patients would perform under a PID-based closed loop system which forms the basis for the first commercially available hybrid closed loop device. Futhermore, we provide a re-tuning methodology which can potentially improve control for 82% of patients, based on the results of an exhaustive reachability analysis. Our results demonstrate that simple nondeterministic models allow us to efficiently tune key controller parameters, thus paving the way for interesting clinical translational applications.