Robust Machine Learning in Critical Care -- Software Engineering and Medical Perspectives
This addresses the problem of integrating machine learning into critical care for improved patient monitoring, but it is incremental as it focuses on collaboration processes rather than novel technical breakthroughs.
The paper tackled the challenge of establishing effective collaboration between software engineers and physicians to design robust machine learning systems for monitoring critically ill patients, such as those undergoing carotid endarterectomy, with results outlining key considerations for team setup, development processes, and system construction.
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.