Robust Machine Learning in Critical Care -- Software Engineering and\n Medical Perspectives

Abstract

Using machine learning in clinical practice poses hard requirements on\nexplainability, reliability, replicability and robustness of these systems.\nTherefore, developing reliable software for monitoring critically ill patients\nrequires close collaboration between physicians and software engineers.\nHowever, these two different disciplines need to find own research perspectives\nin order to contribute to both the medical and the software engineering domain.\nIn this paper, we address the problem of how to establish a collaboration where\nsoftware engineering and medicine meets to design robust machine learning\nsystems to be used in patient care. We describe how we designed software\nsystems for monitoring patients under carotid endarterectomy, in particular\nfocusing on the process of knowledge building in the research team. Our results\nshow what to consider when setting up such a collaboration, how it develops\nover time and what kind of systems can be constructed based on it. We conclude\nthat the main challenge is to find a good research team, where different\ncompetences are committed to a common goal.\n

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