Quantum-Enhanced Serverless Cloud Framework for IoT-Based Healthcare AI-Powered Rule Optimization and Smart Decision Support
DOI:
https://doi.org/10.15662/IJEETR.2022.0406004Keywords:
IoT healthcare, serverless cloud computing, quantum machine learning, business rule automation, intelligent decision support, hybrid quantum classical inference, rule engine optimisationAbstract
In the era of connected healthcare, the convergence of IoT‑driven systems, cloud infrastructures and emerging computational paradigms offers new opportunities for intelligent decision support. This study presents a novel framework — a “Quantum Machine Learning–Empowered Serverless Cloud Framework for IoT‑Driven Healthcare Systems” — which integrates quantum‑machine‑learning (QML) techniques and AI‑based business‑rule optimisation for intelligent decision support. In the proposed architecture, IoT‑enabled medical devices continuously stream patient data into a serverless cloud pipeline, where preprocessing, feature extraction and hybrid quantum‑classical inference models are applied. Concurrently, an automated business‑rule engine dynamically optimises and executes care processes, alerts, triage decisions and workflow logic. The integration of QML supports high‑dimensional, complex healthcare data analysis (e.g., streaming vitals, wearable sensors, genomics), while serverless infrastructure ensures elasticity and cost‑efficiency. The business‑rule optimisation layer ensures that inference outputs translate into actionable, auditable decisions aligned with clinical protocols and organisational policies. We report on a simulation study demonstrating reductions in end‑to‑end latency, improvements in decision‑support accuracy compared to classical baselines, and enhanced rule‑engine throughput under variable IoT load. We also examine key trade‑offs including quantum‑hardware readiness, rule‑engine maintainability, data‑governance and cloud security constraints. Our results suggest that such a hybrid architecture can serve as a powerful next‑generation platform for IoT‑driven healthcare decision support—but also highlight substantial practical challenges
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