AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance.

Artificial intelligence (AI) is a rapidly advancing area with growing clinical applications in healthcare. The neonatal intensive care unit (NICU) produces large amounts of multidimensional data allowing AI and machine learning (ML) new avenues to improve early diagnosis, enhance monitoring, and provide highly-targeted treatment approaches. In this article, we review recent clinical applications of AI to important neonatal problems, including sepsis, retinopathy of prematurity, bronchopulmonary dysplasia, and others.

Comparing machine learning techniques for neonatal mortality prediction: insights from a modeling competition.

Background: Predicting mortality risk in neonatal intensive care units (NICUs) is challenging due to complex, variable clinical and physiological data. Machine learning (ML) offers potential for more accurate risk stratification.

Objective: To compare the performance of various ML models in predicting NICU mortality using a team-based modeling competition.

Cardiorespiratory Stability in Critically Ill Preterm Infants following Dexmedetomidine Initiation.

Objective:  This study aimed to evaluate cardiorespiratory status in preterm infants receiving dexmedetomidine using high-resolution physiologic data.

Study Design:  We analyzed preterm infants with continuous heart rate (HR) and oxygen saturation (SpO) data for 24 hours preceding and 48 hours following dexmedetomidine initiation. Invasive arterial blood pressure (ABP), when available, was analyzed.

Clinical correlates of a high cardiorespiratory risk score for very low birth weight infants.

Background: A pulse oximetry warning system (POWS) to analyze heart rate and oxygen saturation data and predict risk of sepsis was developed for very low birth weight (VLBW) infants.

Methods: We determined the clinical correlates and positive predictive value (PPV) of a high POWS score in VLBW infants. In a two-NICU retrospective study, we identified times when POWS increased above 6 (POWS spike).

Estimation of gestational age-specific reference intervals for coagulation assays in a neonatal intensive care unit using real-world data.

Background: Interpretation of coagulation testing in neonates currently relies on reference intervals (RIs) defined from older patient cohorts. Direct RI studies are difficult, but indirect estimation may allow us to infer normative neonatal distributions from routinely collected clinical data.

Objective: Assess the utility of indirect reference interval methods in estimating coagulation reference intervals in critically ill neonates.