2 research outputs found
In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation
Modern treatments for Type 1 diabetes (T1D) use devices known as artificial
pancreata (APs), which combine an insulin pump with a continuous glucose
monitor (CGM) operating in a closed-loop manner to control blood glucose
levels. In practice, poor performance of APs (frequent hyper- or hypoglycemic
events) is common enough at a population level that many T1D patients modify
the algorithms on existing AP systems with unregulated open-source software.
Anecdotally, the patients in this group have shown superior outcomes compared
with standard of care, yet we do not understand how safe any AP system is since
adverse outcomes are rare. In this paper, we construct generative models of
individual patients' physiological characteristics and eating behaviors. We
then couple these models with a T1D simulator approved for pre-clinical trials
by the FDA. Given the ability to simulate patient outcomes in-silico, we
utilize techniques from rare-event simulation theory in order to efficiently
quantify the performance of a device with respect to a particular patient. We
show a 72,000 speedup in simulation speed over real-time and up to 2-10
times increase in the frequency which we are able to sample adverse conditions
relative to standard Monte Carlo sampling. In practice our toolchain enables
estimates of the likelihood of hypoglycemic events with approximately an order
of magnitude fewer simulations.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.0721
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Learning-based methodologies increasingly find applications in
safety-critical domains like autonomous driving and medical robotics. Due to
the rare nature of dangerous events, real-world testing is prohibitively
expensive and unscalable. In this work, we employ a probabilistic approach to
safety evaluation in simulation, where we are concerned with computing the
probability of dangerous events. We develop a novel rare-event simulation
method that combines exploration, exploitation, and optimization techniques to
find failure modes and estimate their rate of occurrence. We provide rigorous
guarantees for the performance of our method in terms of both statistical and
computational efficiency. Finally, we demonstrate the efficacy of our approach
on a variety of scenarios, illustrating its usefulness as a tool for rapid
sensitivity analysis and model comparison that are essential to developing and
testing safety-critical autonomous systems.Comment: NeurIPS 202