Research Summary
This study presents an AI-powered bedside clinical decision support system that predicts neonatal intensive care unit (NICU) length of stay using electronic medical records, nutrition adherence, medication deviations, antenatal and perinatal factors, and clinical diagnoses. Using data from 836 neonatal admissions for model development and 211 admissions for validation, the system demonstrated accurate patient-specific predictions while providing clinicians with real-time visualization of the factors influencing hospitalization duration.
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.