Robust machine learning in critical care - Software engineering and medical perspectives
Paper i proceeding, 2021

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.

software design

critical care

machine learning

data analysis pipeline


Miroslaw Staron

Göteborgs universitet

Helena Odenstedt Herges

Sahlgrenska universitetssjukhuset

Silvana Naredi

Sahlgrenska universitetssjukhuset

Linda Block

Sahlgrenska universitetssjukhuset

Ali El-Merhi

Sahlgrenska universitetssjukhuset

Richard Vithal

Sahlgrenska universitetssjukhuset

Mikael Elam

Göteborgs universitet

Proceedings - 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI, WAIN 2021

62-69 9474414
9781665444705 (ISBN)

1st IEEE/ACM Workshop on AI Engineering - Software Engineering for AI, WAIN 2021
Virtual, Online, ,







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