论文标题
旨在开发安全保证案件以学习支持学习的医学网络物理系统
Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems
论文作者
论文摘要
机器学习（ML）技术已在医疗网络物理系统（MCP）中越来越多地采用，以实现智能医疗保健。确保支持学习的MCP的安全性和有效性是具有挑战性的，因为这样的系统必须考虑到多样化的患者概况和生理动态，并处理运营不确定性。在本文中，我们为ML控制器开发了一种安全保证案例，以学习为基于ML的预测建立信心。我们将人工胰腺系统（AP）详细介绍了安全保证案例，以代表性地应用学习启用了学习的MCP，并通过实施深层神经网络来为APS的预测提供详细的分析。我们检查ML数据的充分性，并使用正式验证分析基于ML的预测的正确性。最后，我们根据本文的经验概述了开放研究问题。
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.
