Neonatal intensive care units generate massive amounts of physiological data from bedside monitors, ventilators, infusion pumps, blood gas analyzers, and other medical devices. Traditionally, these data are manually documented at regular intervals, leading to transcription errors and the loss of valuable high-resolution clinical information.
This paper introduces Neo, an affordable bedside Internet of Things (IoT) device that integrates heterogeneous NICU medical devices into the cloud-based iNICU platform. Neo continuously acquires real-time physiological signals, synchronizes data from multiple vendors, automatically generates nursing charts, and provides clinicians with unified patient trends through web and tablet interfaces.
The platform combines embedded hardware, cloud infrastructure, and machine learning to support early prediction of neonatal morbidity using continuous physiological signals such as heart rate, respiratory rate, and oxygen saturation. Validation using data collected from 92 preterm infants demonstrated the feasibility of real-time clinical analytics for supporting early intervention and improving neonatal care.
Neo established one of the earliest vendor-agnostic bedside device integration platforms specifically designed for neonatal intensive care, laying the foundation for AI-assisted neonatal monitoring systems.