High-throughput analysis of multimodal monitoring data: the role of machine learning in early warning systems for high-risk neonates

The neonatal intensive care unit (NICU) generates vast amounts of high-throughput, multimodal monitoring data, offering unprecedented potential for identifying early signs of clinical deterioration in high-risk neonates. However, the traditional threshold-based alarm systems are plagued by high false alarm rates and alarm fatigue, failing to harness this data complexity. This narrative review examines the role of machine learning (ML) in transforming early warning systems (EWSs) by effectively analysing these complex data streams. We first characterise the diverse sources-including physiological waveforms, neuromonitoring signals, electronic health records and emerging behavioural data-and inherent challenges (eg, noise, heterogeneity, label scarcity) of NICU data. We then detail key ML technologies, from preprocessing and feature engineering to core algorithms like deep learning models (recurrent neural networks, convolutional neural networks, Transformers) and multimodal fusion strategies, emphasising their application in handling time-series data. The review catalogues empirical evidence of ML-driven EWS for critical conditions such as sepsis, necrotising enterocolitis, neurological injury and cardiorespiratory instability, highlighting performance improvements over conventional methods. Finally, we discuss the significant technical, clinical integration and ethical challenges that impede widespread adoption and outline future directions, including federated learning, digital twins and cloud-edge architectures. The integration of ML-based insights promises to shift neonatal care from a reactive to a proactive, personalised paradigm, ultimately aiming to improve outcomes for vulnerable infants.