Towards data-driven care to optimize neonatal oxygenation

Continuously monitored data from preterm infants, including physiological parameters, contain a great deal of valuable information used suboptimally in current clinical care. It has become increasingly evident that real-time data analytics of physiological data, including AI tools, can play an important role in guiding bedside management. This thesis describes that optimized application of already available data allows for preemptive interventions and precision medicine, thereby improving disease detection and potentially preventing disease. Improved visualization dashboards and clinical decision support systems can bridge the gap between existing knowledge from research and future clinical care