Toward patient safety in closed-loop medical device systems

Arney, David; Pajić, Miroslav; Goldman, Julian M.; Lee, Insup; Mangharam, Rahul; Sokolsky, Oleg

doi:10.1145/1795194.1795214

Cited by 95 publications

(67 citation statements)

References 14 publications

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“…The Patient component is simulated based on given patient physiological reactions when infusion is and is not in progress. The differential equations describing the dynamics are given in [1]. For the purpose of this case study, Table 1.…”

Section: The Pca Examplementioning

confidence: 99%

“…Ctrl reads the patient SpO2, computes the ticket, and outputs the calculated ticket. The ticket-SpO2 relation given in [1] is pre-calculated and shown in Figure 2 with values for SpO2 in the range 65%-80%.…”

Section: The Pca Examplementioning

confidence: 99%

“…We denote T X the set of all possible traces over a set X. 1 The projection π Y (s) of a snapshot s for X on to a subset Y ⊆ X is a snapshot for domain Y such that ∀y ∈ Y. π Y (s) (y) = s(y). The projection …”

Section: Definition Of the Causality Analysis Problemmentioning

confidence: 99%

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A Hybrid Approach to Causality Analysis

Wang

Geoffroy

Gößler

et al. 2015

Runtime Verification

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In component-based safety-critical systems, when a system safety property is violated, it is necessary to analyze which components are the cause. Given a system execution trace that exhibits component faults leading to a property violation, our causality analysis formalizes a notion of counterfactual reasoning (\what would the system behavior be if a component had been correct?") and algorithmically derives such alternative system behaviors, without re-executing the system itself. In this paper, we show that we can improve precision of the analysis if 1) we can emulate execution of components instead of relying on their contracts, and 2) take into consideration input/output dependencies between components to avoid blaming components for faults induced by other components. We demonstrate the utility of the extended analysis with a case study for a closed-loop patient-controlled analgesia system. Abstract. In component-based safety-critical systems, when a system safety property is violated, it is necessary to analyze which components are the cause. Given a system execution trace that exhibits component faults leading to a property violation, our causality analysis formalizes a notion of counterfactual reasoning ("what would the system behavior be if a component had been correct?") and algorithmically derives such alternative system behaviors, without re-executing the system itself. In this paper, we show that we can improve precision of the analysis if 1) we can emulate execution of components instead of relying on their contracts, and 2) take into consideration input/output dependencies between components to avoid blaming components for faults induced by other components. We demonstrate the utility of the extended analysis with a case study for a closed-loop patient-controlled analgesia system.

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Section: The Pca Examplementioning

confidence: 99%

Section: The Pca Examplementioning

confidence: 99%

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A Hybrid Approach to Causality Analysis

Wang

Geoffroy

Gößler

et al. 2015

Runtime Verification

Self Cite

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“…GCS can be integrated into larger simulations in order to support the model driven development of closed loop medical systems, similar to the general approach described in [2].…”

Section: Gcs: Glycemic Control Simulatormentioning

confidence: 99%

On effective testing of health care simulation software

Murphy

Raunak

King

et al. 2011

Proceedings of the 3rd Workshop on Software Engineering in Health Care

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Health 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.

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“…For medical equipment, Lee [9] describes the need for the accurate modeling of patients to deliver higher-confidence medical devices. Arney [1] uses a patient model consisting of drug absorption levels and patient vitals to test a closed-loop system of medical devices including a PCA pump and pulse oximeter. Lee presents a closed-loop artificial pancreas design [8], which is validated through the use of a human diabetic subject simulator [7].…”

Section: Introductionmentioning

confidence: 99%

MEDS: Mockup Electronic Data Sheets for automated testing of cyber-physical systems using digital mockups

Miller¹,

Vahid²,

Givargis³

2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-Cyber-physical systems have become more difficult to test as hardware and software complexity grows. The increased integration between computing devices and physical phenomena demands new techniques for ensuring correct operation of devices across a broad range of operating conditions. Manual test methods, which involve test personnel, require much effort and expense and lengthen a device's time to market. We describe a method for test automation of devices wherein a device is connected to a digital mockup of the physical environment, where both the device and the digital mockup are managed by PC-based software. A digital mockup consists of a behavioral model of the interacting environment, such as a medical ventilator device connected to a digital mockup of human lungs. We introduce Mockup Electronic Data Sheets (MEDS) as a method for embedding model information into the digital mockup, allowing PC software to automatically detect configurable model parameters and facilitate test automation. We summarize a case study showing the effectiveness of digital mockups and MEDS as a framework for test automation on a medical ventilator, resulting in 5x less time spent testing compared to methods requiring test personnel.

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Toward patient safety in closed-loop medical device systems

Cited by 95 publications

References 14 publications

A Hybrid Approach to Causality Analysis

A Hybrid Approach to Causality Analysis

On effective testing of health care simulation software

MEDS: Mockup Electronic Data Sheets for automated testing of cyber-physical systems using digital mockups

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