  The development of respiratory failure is common among patients in intensive
care units (ICU). Large data quantities from ICU patient monitoring systems
make timely and comprehensive analysis by clinicians difficult but are ideal
for automatic processing by machine learning algorithms. Early prediction of
respiratory system failure could alert clinicians to patients at risk of
respiratory failure and allow for early patient reassessment and treatment
adjustment. We propose an early warning system that predicts moderate/severe
respiratory failure up to 8 hours in advance. Our system was trained on
HiRID-II, a data-set containing more than 60,000 admissions to a tertiary care
ICU. An alarm is typically triggered several hours before the beginning of
respiratory failure. Our system outperforms a clinical baseline mimicking
traditional clinical decision-making based on pulse-oximetric oxygen saturation
and the fraction of inspired oxygen. To provide model introspection and
diagnostics, we developed an easy-to-use web browser-based system to explore
model input data and predictions visually.
