  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.
