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

TL;DR

This paper explores how collaboration between physicians and software engineers can develop robust machine learning systems for critical care, emphasizing the importance of interdisciplinary teamwork for reliable patient monitoring.

Contribution

It provides insights into establishing effective interdisciplinary collaboration for designing trustworthy machine learning systems in healthcare.

Findings

Successful collaboration depends on multidisciplinary team commitment.

Development of monitoring systems benefits from shared knowledge building.

Key considerations include team composition and evolving research processes.

Abstract

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.