Real-Time Survival Risk Prediction with Streaming Big Health Data: A Scalable Architecture

Abstract

The healthcare sector faces significant changes as massive data generation grows, which offers new opportunities to improve patient care and resource management. Real-time survival risk prediction models utilize streaming big health data capabilities to enable personalized treatment strategies and proactive interventions that significantly improve patient outcomes. Dynamic assessment and updating patient risk scores when new information emerges proves vital for time-sensitive clinical environments, including emergency departments, intensive care units, and remote patient monitoring programs. Modern healthcare data streams that involve electronic health record updates, laboratory results, vital signs, and sensor data present challenges that traditional batch processing methods cannot adequately address due to their limitations in handling high velocity and volume, as well as diverse data types. Healthcare data streaming challenges require scalable and robust architectures that can manage large volumes of data and provide real-time survival risk predictions. Machine learning applications in healthcare depend on reliable data collection methods, essential for clinicians who require fast and precise information to deliver top-notch patient care. Healthcare settings require careful evaluation of tools, infrastructure, and regulatory standards for machine learning model deployment because behavioral and temporal data shifts add complexity to the process. With the widespread adoption of Electronic Health Records, healthcare providers now routinely collect data needed to develop clinical tools, which require adaptable prediction methods that handle EHR data constraints while updating predictions dynamically and focusing on individual patient clinical contexts.