重症监护中的鲁棒机器学习--软件工程和医学展望
Robust Machine Learning in Critical Care -- Software Engineering and Medical Perspectives
摘要：
在临床实践中使用机器学习对这些系统的可解释性、可靠性、可复制性和鲁棒性提出了严格的要求。因此，开发用于监测危重患者的可靠软件需要医生和软件工程师之间的密切合作。然而，这两个不同的学科需要找到自己的研究视角，以有助于医学和软件工程领域。在本文中，我们解决的问题是如何建立一个合作的地方，软件工程和医学满足设计强大的机器学习系统用于病人护理。我们描述了我们如何设计的软件系统，用于监测颈动脉内膜切除术下的患者，特别是集中在研究团队的知识建设的过程。我们的研究结果显示了在建立这样的合作时应该考虑什么，它是如何随着时间的推移而发展的，以及基于它可以构建什么样的系统。我们得出结论，主要的挑战是找到一个好的研究团队，其中不同的能力致力于一个共同的目标。
英文摘要：
Using machine learning in clinical practice poses hard requirements on explainability, reliability, replicability and robustness of these systems. Therefore, developing reliable software for monitoring critically ill patients requires close collaboration between physicians and software engineers. However, these two different disciplines need to find own research perspectives in order to contribute to both the medical and the software engineering domain. In this paper, we address the problem of how to establish a collaboration where software engineering and medicine meets to design robust machine learning systems to be used in patient care. We describe how we designed software systems for monitoring patients under carotid endarterectomy, in particular focusing on the process of knowledge building in the research team. Our results show what to consider when setting up such a collaboration, how it develops over time and what kind of systems can be constructed based on it. We conclude that the main challenge is to find a good research team, where different competences are committed to a common goal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
