The intensive care unit (ICU) of a hospital looks after the most medically vulnerable patients, many of whom require organ support, such as mechanical ventilation or dialysis. While always critical, the demand on ICU services during the COVID-19 pandemic has further underscored the importance of data-driven decision-making in healthcare.

Furthermore, the ability to accurately predict the clinical outcomes of ICU patients has the potential to guide therapy and may inform decisions about most effective care, including staffing and triage support.

Applying machine learning (ML) to electronic health records (EHRs) has shown promise in predicting clinical outcomes.

However, many of these ML models are based on single-task learning (ST), where the models are trained only to predict a specific adverse event, such as an organ dysfunction or the need for a life-support intervention.

Of greater benefit would be to train multi-task models, which take into account a variety of competing risks along with the interdependencies between organ systems that factor into patient outcomes in a realistic setting.

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