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Updated: Jan 10, 2026
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Neonatal artificial intelligence and machine learning mortality prediction modeling: A systematic review for risk
Chelsea K Bitler1, C Briana Bertoni2, Brian C King3
1Neonatal-Perinatal Medicine Fellowship, Division of Newborn Medicine, UPMC Children's Hospital of Pittsburgh/Magee Women's Hospital, 300 Halket St, Pittsburgh, PA, 15213, USA; Clinical Informatics Fellowship, Division of Internal Medicine, UPMC Presbyterian, Pittsburgh, PA, USA.
Abstract:
Mortality remains a key indicator for the assessment of care quality in medicine. In neonatology, mortality rates are highly variable, both across units and over time. Comparison of crude mortality rates, however, are insufficient for benchmarking, as they fail to account for differences in population case mix and severity of illness. Risk adjustment using artificial intelligence (AI) and machine learning (ML) has emerged as a promising tool to facilitate meaningful comparisons and drive improvement. This review seeks to examine the state of the current literature on the use of AI/ML-based models to predict mortality in the neonatal intensive care unit (NICU). We identified 37 studies describing 242 models. Most studies developed models using single-center data and frequently lacked external validation. Similarly, reporting of performance metrics was heterogenous, limiting evaluation. As a result, further work is necessary before AI/ML-enabled risk adjustment is feasible.
