Dynamic Risk Management for Cooperative Autonomous Medical Cyber-Physical Systems
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.
KeywordsMedical cyber-physical systems System of systems Adaptive systems Cooperative system Autonomous systems Runtime risk management Modular safety certification Risk assessment
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.
- 3.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_26CrossRefGoogle Scholar
- 4.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)Google Scholar
- 5.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)Google Scholar
- 7.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_9CrossRefGoogle Scholar
- 8.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_20CrossRefGoogle Scholar
- 10.Medawar, S., Scholle, D., Sljivo, I.: Cooperative safety critical CPS platooning in SafeCOP. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO)Google Scholar
- 11.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)Google Scholar
- 13.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)Google Scholar
- 14.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 2016Google Scholar
- 17.Practices institute for safe medication: fatal PCA adverse events continue to happen… Better patient monitoring is essential to prevent harm, 41, 736–738 (2013)Google Scholar
- 18.Jensen, F.V.: An introduction to Bayesian networks. Springer, Heidelberg (1996)Google Scholar
- 19.Ross, T.J. (University of N.M.): Fuzzy logic with engineering applications. Wiley, Chichester (2010)Google Scholar