Machine Learning Applications in the ICU: Opportunities and Pitfalls

Intensive Care Units (ICUs) care for the sickest patients in any given hospital system and are associated with the worst outcomes and healthcare-associated costs. There is great interest in tailoring or streamlining ICU care to improve outcomes and reduce costs. With the growing use of machine learning (ML) in healthcare research, there has been increased utilization of ML-based approaches to address questions in ICU care. This necessitates an approach that considers the opportunities and pitfalls inherent to these tools. This paper aims to provide a brief overview of ML approaches to ICU datasets before outlining the opportunities for ML applications in this area and describing key pitfalls that arise when using ML tools to solve ICU and healthcare-related problems, namely that the current methods of addressing class imbalances and data missingness in healthcare datasets are at risk of causing deviations between model data and real-world signals, and that the resulting models are often difficult to apply to patient care. The contribution of this paper is to critically examine these pitfalls and provide guidance in the use of ML in ICU applications, such that solutions can be designed in a way that accounts for these potential weaknesses.