Figure 1.

Development of a machine learning-based system for prediction of apnoeic events in preterm infants. Solid lines, Steps already undertaken to develop a predictive system; Dashed lines, Future steps for development and application of a machine learning system to predict apnoeic events. Patient demographics: may include birth gestation, postnatal age, birth and current weight, Apgar scores, and medical diagnoses. Clinical characteristics: may include mode, duration and level of respiratory support, dosage and timing of previous and current treatment (e.g., surfactant therapy, post-natal corticosteroid, caffeine), and investigation findings (e.g., hemoglobin, radiographic findings). Physiological information: may include heart rate and R-R interval, respiratory rate and inter-breath interval, movement patterns, and characteristics of previous apnoeic events (e.g., type and duration of event, latency between events, apnoea-associated destabilization). Clinical action: may include an early warning display, a servo-controlled stimulus to maintain respiratory cadence, or decision support regarding further preventative management.