Chen, Sanjian
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Publication Evaluation and Enhancement of an Intraoperative Insulin Infusion Protocol via In-Silico Simulation(2013-09-09) Kohl, Benjamin A.; Chen, Sanjian; Mullen-Fortino, Margaret; Lee, InsupIntraoperative glycemic control, particularly in cardiac surgical patients, remains challenging. Patients with impaired insulin sensitivity and/or secretion (i.e., type 1 diabetes mellitus) often manifest extremely labile blood glucose measurements during periods of stress and inflammation. Most current insulin infusion protocols are developed based on clinical experiences and consensus among a local group of physicians. Recent advances in human glucose metabolism modeling have established a computer model that invokes algorithms representing many of the pathways involved in glucose dysregulation for patients with diabetes. In this study, we used an FDA approved glucose metabolism model to evaluate an existing institutional intraoperative insulin infusion protocol via closedloop simulation on the virtual diabetic population that comes with the computer model. A comparison of simulated responses to actual retrospective clinical data from 57 type 1 diabetic patients undergoing cardiac surgery managed by the institutional protocol was performed. We then designed a proportional-derivative controller that overcomes the weaknesses exhibited by our old protocol while preserving its strengths. In-silico evaluation results show that our proportional-derivative controller more effectively manages intraoperative hyperglycemia while simultaneously reducing hypoglycemia and glycemic variability. By performing insilico simulation on intraoperative glucose and insulin responses, robust and seemingly efficacious algorithms can be generated that warrant prospective evaluation in human subjects.Publication A Data-Driven Behavior Modeling and Analysis Framework for Diabetic Patients on Insulin Pumps(2015-10-01) Chen, Sanjian; Feng, Lu; Rickels, Michael R.; Peleckis, Amy; Sokolsky, Oleg; Lee, InsupAbout 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control. In this paper, we propose an “Eat, Trust, and Correct” (ETC) framework to model the T1D insulin pump users’ behavior. We use machine learning techniques to analyze the user behavior from a clinical dataset that we collected on 55 T1D patients who use insulin pumps. We demonstrate the usefulness of the ETC behavior modeling framework by performing in silico experiments. To this end, we integrate the user behavior model with an individually parameterized glucose physiological model, and perform probabilistic model checking on the user-in-the-loop system. The experimental results show that switching behavior types can significantly improve a patient’s glycemic control outcomes. These analysis results can boost the effectiveness of T1D patient education and peer support.Publication Removing Abstraction Overhead in the Composition of Hierarchical Real-Time System(2011-04-01) Chen, Sanjian; Phan, Linh T.X.; Lee, Jaewoo; Lee, Insup; Sokolsky, OlegThe hierarchical real-time scheduling framework is a widely accepted model to facilitate the design and analysis of the increasingly complex real-time systems. Interface abstraction and composition are the key issues in the hierarchical scheduling framework analysis. Schedulability is essential to guarantee that the timing requirements of all components are satisfied. In order for the design to be resource efficient, the composition must be bandwidth optimal. Associativity is desirable for open systems in which components may be added or deleted at run time. Previous techniques on compositional scheduling are either not resource efficient in some aspects, or cannot achieve optimality and associativity at the same time. In this paper, several important properties regarding the periodic resource model are identified. Based on those properties, we propose a novel interface abstraction and composition framework which achieves schedulability, optimality, and associativity. Our approach eliminates abstraction overhead in the composition.Publication Realizing Compositional Scheduling Through Virtualization(2012-04-01) Lee, Jaewoo; Xi, Sisu; Chen, Sanjian; Phan, Linh T.X.; Gill, Chris; Lee, Insup; Lu, Chenyang; Sokolsky, OlegWe present a co-designed scheduling framework and platform architecture that together support compositional scheduling of real-time systems. The architecture is built on the Xen virtualization platform, and relies on compositional scheduling theory that uses periodic resource models as component interfaces.We implement resource models as periodic servers and consider enhancements to periodic server design that significantly improve response times of tasks and resource utilization in the system while preserving theoretical schedulability results. We present an extensive evaluation of our implementation using workloads from an avionics case study as well as synthetic ones.Publication Improving Resource Utilization for Compositional Scheduling Using DPRM Interfaces(2010-11-30) Lee, Jaewoo; Phan, Linh T.X.; Chen, Sanjian; Sokolsky, Oleg; Lee, InsupThe paper revisits the generation of interfaces for compositional real-time scheduling. Following an established line of research, we use periodic resource models in component interfaces to describe resource demand of the component. We identify a deficiency of existing interface generation algorithms that may require parameters of the resource model to be infeasibly small. We propose a new algorithm for interface generation that avoids this deficiency. We further demonstrate that resource utilization can be improved by using dual-periodic resource model (DPRM) interfaces that employ two periodic resource models to characterize the resource demand more precisely.Publication On Effective Testing of Health Care Simulation Software(2011-05-01) Murphy, Christian; Raunak, M. S.; King, Andrew; Chen, Sanjian; Imbriano, Christopher; Kaiser, Gail; Lee, Insup; Sokolsky, Oleg; Clarke, Lori; Osterweil, LeonHealth care professionals rely on software to simulate anatomical and physiological elements of the human body for purposes of training, prototyping, and decision making. Software can also be used to simulate medical processes and protocols to measure cost effectiveness and resource utilization. Whereas much of the software engineering research into simulation software focuses on validation (determining that the simulation accurately models real-world activity), to date there has been little investigation into the testing of simulation software itself, that is, the ability to effectively search for errors in the implementation. This is particularly challenging because often there is no test oracle to indicate whether the results of the simulation are correct. In this paper, we present an approach to systematically testing simulation software in the absence of test oracles, and evaluate the effectiveness of the technique.Publication CARTS: A Tool for Compositional Analysis of Real-Time Systems(2010-11-01) Phan, Linh T.X.; Lee, Jaewoo; Easwaran, Arvind; Ramaswamy, Vinay; Chen, Sanjian; Lee, Insup; Sokolsky, OlegThis paper demonstrates CARTS, a compositional analysis tool for real-time systems. We presented an overview of the underlying theoretical foundation and the architecture design of the tool. CARTS is open source and available for free download at http://rtg.cis.upenn.edu/carts/.Publication Parameter-Invariant Monitor Design for Cyber Physical Systems(2018-01-01) Weimer, James; Ivanov, Radoslav; Chen, Sanjian; Roederer, Alexander; Sokolsky, Oleg; Lee, InsupThe tight interaction between information technology and the physical world inherent in Cyber-Physical Systems (CPS) can challenge traditional approaches for monitoring safety and security. Data collected for robust CPS monitoring is often sparse and may lack rich training data describing critical events/attacks. Moreover, CPS often operate in diverse environments that can have significant inter/intra-system variability. Furthermore, CPS monitors that are not robust to data sparsity and inter/intra-system variability may result in inconsistent performance and may not be trusted for monitoring safety and security. Towards overcoming these challenges, this paper presents recent work on the design of parameter-invariant (PAIN) monitors for CPS. PAIN monitors are designed such that unknown events and system variability minimally affect the monitor performance. This work describes how PAIN designs can achieve a constant false alarm rate (CFAR) in the presence of data sparsity and intra/inter system variance in real-world CPS. To demonstrate the design of PAIN monitors for safety monitoring in CPS with different types of dynamics, we consider systems with networked dynamics, linear-time invariant dynamics, and hybrid dynamics that are discussed through case studies for building actuator fault detection, meal detection in type I diabetes, and detecting hypoxia caused by pulmonary shunts in infants. In all applications, the PAIN monitor is shown to have (significantly) less variance in monitoring performance and (often) outperforms other competing approaches in the literature. Finally, an initial application of PAIN monitoring for CPS security is presented along with challenges and research directions for future security monitoring deployments.Publication Data-driven Adaptive Safety Monitoring using Virtual Subjects in Medical Cyber-Physical Systems: A Glucose Control Case Study(2016-09-01) Chen, Sanjian; Sokolsky, Oleg; Weimer, James; Lee, InsupMedical cyber-physical systems (MCPS) integrate sensors, actuators, and software to improve patient safety and quality of healthcare. These systems introduce major challenges to safety analysis because the patient’s physiology is complex, nonlinear, unobservable, and uncertain. To cope with the challenge that unidentified physiological parameters may exhibit short-term variances in certain clinical scenarios, we propose a novel run-time predictive safety monitoring technique that leverages a maximal model coupled with online training of a computational virtual subject (CVS) set. The proposed monitor predicts safety-critical events at run-time using only clinically available measurements. We apply the technique to a surgical glucose control case study. Evaluation on retrospective real clinical data shows that the algorithm achieves 96% sensitivity with a low average false alarm rate of 0.5 false alarm per surgery.Publication Towards Assurance for Plug & Play Medical Systems(2015-09-01) King, Andrew L.; Feng, Lu; Chen, Sanjian; Sokolsky, Oleg; Lee, Insup; Procter, Sam; Hatcliff, JohnTraditional safety-critical systems are designed and integrated by a systems integrator. The system integrator can asses the safety of the completed system before it is deployed. In medicine, there is a desire to transition from the traditional approach to a new model wherein a user can combine various devices post-hoc to create a new composite system that addresses a specific clinical scenario. Ensuring the safety of these systems is challenging: Safety is a property of systems that arises from the interaction of system components and it’s not possible to asses overall system safety by assessing a single component in isolation. It is unlikely that end-users will have the engineering expertise or resources to perform safety assessments each time they create a new composite system. In this paper we describe a platform-oriented approach to providing assurance for plug & play medical systems as well as an associated assurance argument pattern.