the application of machine learning in the neonatal intensive care unit: an integrated review

Introduction Neonatal Intensive Care Units (NICUs) are faced with complex challenges in managing severely ill newborns, where proper and timely clinical decisions can potentially optimize outcomes. Machine learning (ML) is a groundbreaking aspect in neonatology to leverage vast amounts of data from electronic health records (EHRs), vital signs, and laboratory results to enhance decision-making, predict adverse outcomes, and streamline resource utilization. With the analysis of trends in complex, time-associated data, ML has the potential for early sepsis diagnosis, length of stay prediction, and mortality risk estimation. In this integrative review, the uses of ML in NICU will be discussed with an eye to its role in clinical decision-making support, disease diagnosis, and resource allocation, as well as problems and recommendations for the future. Methodology Systematic search was conducted on PubMed, Scopus, Google Scholar, English-language databases from ۲۰۱۴-۲۰۲۵ time period. Among ۳۰۰ articles identified, duplicate articles were excluded and titles and abstracts screened and ۵۰ articles selected for full-text analysis. Inclusion criteria were ML model-based studies (e.g., Random Forest, logistic regression, ensemble, deep learning) conducted within NICUs of neonates (ages ۰-۲۸ days), with outcomes reported within the domains of clinical decision support (e.g., mortality prediction, length of stay), disease detection (e.g., sepsis, bronchopulmonary dysplasia), or resource optimization (e.g., admission prioritization, real-time monitoring). Results Ensemble methods and Random Forest demonstrated greater accuracy in predicting length of stay, enhancing resource allocation. When it comes to disease diagnosis, ML models like Random Forest and logistic regression performed better than traditional methods in predicting late-onset sepsis, on the basis of vital signs and biomarkers, with some demonstrating early detection up to ۲۴ hours earlier than clinical suspicion. ML improved the prediction of neonatal mortality in respiratory failure beyond conventional scoring systems. Additionally, ML allowed for real-time monitoring and prioritized resource-limited settings according to optimal care provision. Limitations include integrating data from different sources, data quality, and the management of ethical concerns like patient anonymity. Conclusion Machine learning can potentially disrupt the practice of NICU therapy by improving early diagnosis, clinical decision-support, and resource allocation. Promising as it is, in the way of realizing this are data quality, ethics, and generalizability of the models. Research in the future needs to progress towards standardized measures of performance, more embedding of models into clinical workflows, and pragmatic deployment across populations so that the full potential of ML can be realized in neonatal medicine.