Length of stay (LOS) is an important indicator of neonatal outcomes, healthcare resource utilization, and hospital costs. This study developed an artificial intelligence–based prediction model that estimates NICU length of stay using routinely collected electronic medical record data. Unlike previous approaches, the model incorporated antenatal and perinatal characteristics, deviations from ASPEN nutrition guidelines, deviations from NeoFax medication recommendations, and major neonatal clinical diagnoses. Data from 836 neonatal admissions across two tertiary NICUs were used to develop gestation-specific predictive models and validated using an independent cohort of 211 patients. The resulting bedside interface enables clinicians to visualize patient-specific risk factors contributing to prolonged hospitalization, supporting family counseling, clinical decision-making, and operational planning while demonstrating the feasibility of real-time AI-assisted predictive analytics within neonatal intensive care.
Research Area: Predictive Analytics
Predicting Clinical Outcomes Using Artificial Intelligence and Machine Learning in Neonatal Intensive Care Units: A Systematic Review
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming neonatal intensive care by enabling predictive analytics from large volumes of clinical data. This systematic review evaluated 68 published studies examining AI and ML applications for predicting neonatal morbidities, mortality, and length of stay in NICUs. The review analyzed prediction models, data sources, machine learning techniques, and performance metrics while identifying current limitations and future opportunities. The authors propose that integrating multimodal data—including electronic medical records, physiologic monitoring, medical imaging, laboratory results, and environmental sensing—can enable next-generation clinical decision support systems that improve neonatal outcomes through personalized, real-time predictive analytics.