Machine Learning for Neonatal Early Warning Signs

Abstract: Cardio-respiratory dysfunction, sepsis and necrotizing enterocolitis are responsible for a large numberof deaths in the neonatal population. Despite ecient monitoring and screening in Intensive CareUnits, diagnosis prior to clinical symptoms remains a dicult task. Based on Heart Rate Monitoring,the state-of-the-art HeRO system indicates the risk for sepsis and has already proven its ability toreduce mortality in the neonatal ICU. Recent studies have shown that a particular respiratory behaviorknown as ABD-events, can be used as a physiomarker for sepsis and is therefore an early warningsign. Detecting ABD-events is currently done by simple thresholding techniques. Based on cardiorespiratorydata and hindsight from previous patients, we aim at improving the early warning systemby applying machine learning algorithms. Data with higher frequency than those used in the HeROsystem and biological samples are still to be collected, but still, using low frequency data, we managedto obtain a specicity (true positive) of 70% and a sensitivity (true negative) of 65% on manuallylabeled events. In this report, the theoretical framework is presented along with the practical issuesencountered during the project.