The intelligent neonatal healthcare: a systematic review of machine learning architectures integrating the internet of medical things and blockchain
Abstract
Background and motivation: Neonatal healthcare involves managing extreme physiological vulnerability, rapid disease progression, and time-critical decision-making within Neonatal Intensive Care Units (NICUs). Recent developments in blockchain, the Internet of Medical Things (IoMT), and Machine Learning (ML) have provided new opportunities of enhanced health-data governance, non-stop physiological tracking, and prevention of risks. Although this has been achieved, the past studies have primarily evaluated these technologies separately or with regard to adult health care conditions with little consideration of their combined relevance to neonatal care.
Methods: In this study, a PRISMA-guided systematic review was conducted to examine intelligent neonatal healthcare systems that integrate ML, IoMT, and blockchain technologies. A systematic search of major scientific databases identified 122 records, of which 76 studies satisfied predefined inclusion criteria and were included for qualitative synthesis. The selected studies were discussed according to clinical areas of application, system architecture, evaluation practices, and implementation limitations peculiar to neonatal contexts.
Synthesis of current evidence and identified research gaps: The review indicates that ML-based approaches are the most mature, particularly for early disease detection, mortality risk prediction, and clinical decision support. Continuous and remote physiological monitoring is the primary use of…
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