Artificial Intelligence for Real-Time Monitoring of Neonatal Vital Signs: Enhancing Decision-Making in Critical Care Units - Sravani, Gogula; Khaleel, Sk.; Haritha, Haritha; Sudhakar, Sunkesula; Vardhan, C. M. Vivek (Journal of Neonatal Surgery, 2025)
This study presents an artificial intelligence (AI)-based framework for the real-time monitoring of neonatal vital signs in Neonatal Intensive Care Units (NICUs), addressing limitations in traditional threshold-based alarm systems. Leveraging LSTM neural networks, the model processes heart rate, respiratory rate, oxygen saturation, and temperature data to detect physiological anomalies with high accuracy. Using data from the MIMIC-III database, the system achieved an average F1-score of 91.3%, outperforming conventional systems in both sensitivity and false alert reduction. It integrates clinician feedback, enabling dynamic adaptation and interpretability through SHAP-based feature attribution. The AI system issues colour-coded alerts and provides transparent explanations for each risk prediction, facilitating faster, more informed decision-making. Real-time implementation tests confirmed operational feasibility, with sub-second latency and minimal resource demands. The system’s closed-loop design, combining prediction, feedback, and continuous learning, makes it a clinically viable tool for improving neonatal outcomes and reducing alarm fatigue in critical care settings