Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring

Abstract

The traditional healthcare system is increasingly challenged by its dependence on inperson consultations and manual monitoring, struggling with issues of scalability, the immediacy of care, and efficient resource allocation. As the global population ages and chronic conditions proliferate, the demand for healthcare systems capable of delivering efficient and remote care is becoming more pressing. In this context, Deep Reinforcement Learning (DRL) emerges as a technological advancement that improves the healthcare by enabling smart, adaptive, and real-time decision-making processes. Existing DRL applications in resource allocation, however, face significant challenges. They often lack the adaptability required to respond to the dynamic and complex nature of healthcare environments, struggle with optimizing latency, and fail to address specific node capacity constraints key factors that impacts the effectiveness of healthcare applications. Addressing these challenges, this paper introduces the Deep Reinforcement Learning for Live Video Transmission (DRL-LVT) framework. This new technique optimizes video resource allocation in Device-to-Device (D2D) networks within healthcare settings. By formulating the video resource allocation challenge as a multi-objective optimization problem, the framework aims to minimize network delays while respecting node capacity limitations. The core of DRLLVT is its novel algorithm that leverages Deep Reinforcement Learning (DRL) to dynamically adapt to changing environmental conditions, facilitating real-time decisions that consider node capacities, latency, and the overall network dynamics. We evaluate the performance of our proposed model and benchmark it against existing state-of-the-art techniques. Our results demonstrate significant improvements in efficiency, reliability, and adaptability, making the DRL-LVT framework a robust solution for real-time remote patient monitoring in smart healthcare systems.

Keywords

• Smart healthcare system
• RPM
• video live streaming
• deep reinforcement learning
• node capacities