Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates. (July 2021) - Baker, Stephanie; Xiang, Wei; Atkinson, Ian
Abstract: Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and resource utilization. However, existing schemes often require laborious medical testing and calculation, and are typically only calculated once at admission. In this work, we propose a shallow hybrid neural network for the prediction of mortality risk in 3-day, 7-day, and 14-day risk windows using only birthweight, gestational age, sex, and heart rate (HR) and respiratory rate (RR) information from a 12-h window. As such, this scheme is capable of continuously updating mortality risk assessment, enabling analysis of health trends and responses to treatment. The highest performing scheme was the network that considered mortality risk within 3 days, with this scheme outperforming state-of-the-art works in the literature and achieving an area under the receiver-operator curve (AUROC) of 0.9336 with standard deviation of 0.0337 across 5 folds of cross-validation. As such, we conclude that our proposed scheme could readily be used for continuously-updating mortality risk prediction in NICU environments. Highlights: A machine learning method for neonatal mortality risk assessment is proposed. The proposed method is interpretable and capable of continuous updating. Variations in heart and respiratory rates over 12 h are used as features. An AUROC of 0.9336 for predicting mortality risk within a 3-day window is achieved.