Specifying real-time properties with metric temporal logic
Real-Time Systems
Statistical probabilistic model checking with a focus on time-bounded properties
Information and Computation
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Robustness of temporal logic specifications
Robustness of temporal logic specifications
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Theoretical Computer Science
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CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
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Bayesian statistical model checking with application to Simulink/Stateflow verification
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
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TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Model-based dependability analysis of programmable drug infusion pumps
FORMATS'11 Proceedings of the 9th international conference on Formal modeling and analysis of timed systems
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Proceedings of the 16th international conference on Hybrid systems: computation and control
From software verification to `everyware' verification
Computer Science - Research and Development
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We present a case study on the use of robustness-guided and statistical model checking approaches for simulating risks due to insulin infusion pump usage by diabetic patients. Insulin infusion pumps allow for a continuous delivery of insulin with varying rates and delivery profiles to help patients self-regulate their blood glucose levels. However, the use of infusion pumps and continuous glucose monitors can pose risks to the patient including chronically elevated blood glucose levels (hyperglycemia) or dangerously low glucose levels (hypoglycemia). In this paper, we use mathematical models of the basic insulin-glucose regulatory system in a diabetic patient, insulin infusion pumps, and the user's interaction with these pumps defined by commonly used insulin infusion strategies for maintaining normal glucose levels. These strategies include common guidelines taught to patients by physicians and certified diabetes educators and have been implemented in commercially available insulin bolus calculators. Furthermore, we model the failures in the devices themselves along with common errors in the usage of the pump. We compose these models together and analyze them using two related techniques: (a) robustness guided state-space search to explore worst-case scenarios and (b) statistical model checking techniques to assess the probabilities of hyper- and hypoglycemia risks. Our technique can be used to identify the worst-case effects of the combination of many different kinds of failures and place high confidence bounds on their probabilities.