Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866929506776776704 |
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| author | Bagheri, Maryam Lamp, Josephine Zhou, Xugui Feng, Lu Alemzadeh, Homa |
| author_facet | Bagheri, Maryam Lamp, Josephine Zhou, Xugui Feng, Lu Alemzadeh, Homa |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2211_15413 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems Bagheri, Maryam Lamp, Josephine Zhou, Xugui Feng, Lu Alemzadeh, Homa Machine Learning Artificial Intelligence Systems and Control 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. |
| title | Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems |
| topic | Machine Learning Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2211.15413 |