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Publications

Journal Article Epublish 1 citation

PubMed: 42697903 DOI: 10.1038/s41598-026-68894-9

Clinical risk-aware reinforcement learning for latency-constrained healthcare IoT scheduling.

Sci Rep 16(1)

Authors: Jawaher Abdullah Bin Jumah, Hyder Osman Mirghani, Saad Alateeq, Muidh Awadh Algahtani, Bushra Yousef Althubyani, Naif Alsaadi, Aisha Abdallah, Malik Bader Alazzam

Affiliations

  • RN, PhD, Assistant Professor, Assistant Vice Dean for Graduate Studies and Scientific Research, College of Nursing, King Saud University, Saudi Arabia.
  • Department of Internal Medicine, Faculty of Medicine, University of Tabuk Saudi Arabia, Prince Fahd Bin Sultan, Tabuk, 51941, Tabuk, Saudi Arabia.
  • Social Studies Department, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi Arabia.
  • Department of Industrial Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia.
  • Clinical Resource Nurse Endoscopy-Organ, Transplant Center King Faisal Specialist Hospital & Research Centre,, Saudi Arabia.
  • Department of Industrial Engineering, Faculty of Engineering Rabigh, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
  • PhD, Assistant professor, Department of Health Information Management and Technology (HIMT), College of Applied Medical Sciences, University of Hafar Albltain, Al-Yasmeen Female Students Complex - Hafar Al-Batin University 6182 Al Manar, Hafar Al-Batin, 39812,, Saudi Arabia.
  • Faculty of Information Technology, Jadara University, Irbid, Jordan.

Abstract

The rapid growth of the Healthcare Internet of Things (HIoT) has enabled continuous, real-time patient monitoring through wearable and bedside devices. These systems generate time-sensitive physiological data that are essential for the early detection of critical conditions such as arrhythmias and hypoxia. However, conventional cloud-centric architectures introduce significant end-to-end latency, which can compromise timely clinical response in safety-critical scenarios. Mobile Edge Computing (MEC) mitigates this limitation by bringing computation closer to data sources; yet, existing scheduling approaches remain largely system-centric and do not adequately incorporate patient-specific clinical risk into their decision-making. To address this gap, this paper presents CRAI-LCS, a clinical risk-aware Reinforcement learning (RL) framework for latency-constrained scheduling in HIoT systems. Unlike prior simulation-driven studies, CRAI-LCS integrates real physiological data from the PhysioNet MIT-BIH Arrhythmia database to construct realistic, data-driven workloads. Specifically, electrocardiogram (ECG) signals are segmented into time-windowed tasks with clinically grounded characteristics, including input size, computational demand, and urgency-aware deadlines. The framework combines data-driven clinical risk estimation, deadline-violation prediction, and RL-based scheduling to dynamically prioritize high-risk tasks while efficiently managing system resources. Experimental results demonstrate that CRAI-LCS consistently outperforms baseline approaches in terms of latency, deadline compliance, and resource utilization under realistic workload conditions. Ablation studies further confirm the individual contributions of the clinical risk-awareness and predictive scheduling components. Overall, these findings highlight the importance of incorporating real physiological data into scheduling design, providing a more reliable and clinically relevant foundation for next-generation healthcare edge intelligence systems.

MeSH

Reinforcement, Psychology, Workload, Learning