Unmanaged pain in hospitalized infants has serious long-term complications. Our international team of knowledge users and health/natural science/engineering/social science researchers have come together to build a machine learning algorithm that will learn how to discriminate invasive and non-invasive distress. A sample of 400 preterm infants (300 from Mount Sinai Hospital and 100 from University College London Hospital [UCLH]) and their mothers will be followed during a routine painful procedure (heel lance). Pain indicators (facial grimacing [behavioural indicators], heart rate, oxygen saturation levels [physiologic indicators], brain electrical activity) during the painful procedure will be used to train the algorithm to discriminate between different types of distress (pain-related and non-pain related).
A multi-national multidisciplinary team will be working collaboratively to build a machine learning algorithm to distinguish between preterm infant distress states in the Neonatal Intensive Care Unit.