Abstract
Medical cyber-physical systems (MCPS) combine independent devices at runtime in order to render new patient monitoring/control functionalities, such as physiological closed loops for controlling drug infusion and optimization of alarms. MCPS and their relevant system contexts are highly variable, which detrimentally affects the application of established safety assurance methodologies. In this paper, we introduce an approach based on dynamic risk assessment and control for MCPS. During runtime, information regarding the safety properties of the constituent systems, relevant information about the patient’s characteristics, as well as other relevant context information is utilized to dynamically and continuously optimize the system performance while guaranteeing an acceptable level of safety. We evaluated our approach by means of a patient-controlled analgesia proof-of-concept simulation and sensitivity analysis.
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Notes
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Specified according to the standard ASTM F2761-2010.
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References
Lee, I., Sokolsky, O., et al.: Challenges and research directions in medical cyber-physical systems. Proc. IEEE. 100, 75–90 (2012)
Schneider, D., Trapp, M.: Conditional safety certification of open adaptive systems. ACM Trans. Auton. Adapt. Syst. 8, 1–20 (2013)
Kurd, Z., Kelly, T., McDermid, J., Calinescu, R., Kwiatkowska, M.: Establishing a framework for dynamic risk management in ‘intelligent’ aero-engine control. In: Buth, B., Rabe, G., Seyfarth, T. (eds.) SAFECOMP 2009. LNCS, vol. 5775, pp. 326–341. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04468-7_26
Machin, M., Guiochet, J., Waeselynck, H., Blanquart, J., Roy, M., Masson, L.: SMOF: A safety monitoring framework for autonomous systems. IEEE Trans. Syst. Man, Cybern. Syst. 1–14 (2016)
Thieme, C.A., Utne, I.B.: A risk model for autonomous marine systems and operation focusing on human–autonomy collaboration. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 231, 446–464 (2017)
Wardziński, A.: Safety assurance strategies for autonomous vehicles. In: Harrison, M.D., Sujan, M.-A. (eds.) SAFECOMP 2008. LNCS, vol. 5219, pp. 277–290. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87698-4_24
Feth, P., Schneider, D., Adler, R.: A conceptual safety supervisor definition and evaluation framework for autonomous systems. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10488, pp. 135–148. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66266-4_9
Leite, F.L., Adler, R., Feth, P.: Safety assurance for autonomous and collaborative medical cyber-physical systems. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10489, pp. 237–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66284-8_20
Wei, R., Kelly, T.P., Hawkins, R., Armengaud, E.: DEIS: dependability engineering innovation for cyber-physical systems. In: Seidl, M., Zschaler, S. (eds.) STAF 2017. LNCS, vol. 10748, pp. 409–416. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74730-9_37
Medawar, S., Scholle, D., Sljivo, I.: Cooperative safety critical CPS platooning in SafeCOP. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO)
Cremer, F., Den Breejen, E., Schutte, K.: Sensor data fusion for anti-personnel land-mine detection. In: Proceedings of EuroFusion 1998, International Conference on Data Fusion, pp. 55–60 (1998)
Challa, S., Koks, D.: Bayesian and Dempster-Shafer fusion. Sadhana 29, 145–174 (2004)
Stevens, N., et al.: Smart alarms: multivariate medical alarm integration for post CABG surgery patients. In: Proceedings of the 2nd ACM SIGHIT - IHI 2012, p. 533. ACM Press, New York (2012)
Jiang, Y., Tan, P., Song, H., Wan, B., Hosseini, M., Sha, L.: A self-adaptively evolutionary screening approach for sepsis patient. In: IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), pp. 60–65, August 2016
Brito, M., Griffiths, G.: A bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions. Reliab. Eng. Syst. Saf. 146, 55–67 (2016)
Lynn, L.A., Curry, J.P.: Patterns of unexpected in-hospital deaths: a root cause analysis. Patient Saf. Surg. 5, 3 (2011)
Practices institute for safe medication: fatal PCA adverse events continue to happen… Better patient monitoring is essential to prevent harm, 41, 736–738 (2013)
Jensen, F.V.: An introduction to Bayesian networks. Springer, Heidelberg (1996)
Ross, T.J. (University of N.M.): Fuzzy logic with engineering applications. Wiley, Chichester (2010)
Acknowledgments
The ongoing research that led to this paper is funded by the Brazilian National Research Council (CNPq) under grant CSF 201715/2014-7 in cooperation with Fraunhofer IESE and TU Kaiserslautern. We would also like to thank Sonnhild Namingha for proofreading.
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Leite, F.L., Schneider, D., Adler, R. (2018). Dynamic Risk Management for Cooperative Autonomous Medical Cyber-Physical Systems. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2018. Lecture Notes in Computer Science(), vol 11094. Springer, Cham. https://doi.org/10.1007/978-3-319-99229-7_12
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