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arXiv · 2211.15413

Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems

Maryam Bagheri·Josephine Lamp·Xugui Zhou·Lu Feng·Homa Alemzadeh
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Abstract

Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions. We present the safety assurance case in detail for Artificial Pancreas Systems (APS) as a representative application of learning-enabled MCPS, and provide a detailed analysis by implementing a deep neural network for the prediction in APS. We check the sufficiency of the ML data and analyze the correctness of the ML-based prediction using formal verification. Finally, we outline open research problems based on our experience in this paper.

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Maryam Bagheri, Josephine Lamp, Xugui Zhou, Lu Feng, Homa Alemzadeh. 2022-12-19. Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems. https://arxiv.org/abs/2211.15413

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