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Abstract
The advent of machine learning (ML) has revolutionized healthcare, particularly in critical care and patient monitoring systems. This study explores the integration of ML for real-time big data analytics to enhance patient outcomes in intensive care units (ICUs). It presents a synthesis of recent advancements, highlighting the benefits of predictive analytics, anomaly detection, and decision support systems. By analyzing data from published studies, we discuss challenges such as data heterogeneity, security concerns, and model interpretability. Practical recommendations for improving real-time analytics through advanced ML models are provided. This paper contributes to a growing body of literature underscoring the transformative potential of ML in critical care.
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