A Unified Framework for Differentiated Services in Intelligent Healthcare Systems
المؤلف | Al-Abbasi, A. O. |
المؤلف | Samara, L. |
المؤلف | Salem, S. |
المؤلف | Hamila, R. |
المؤلف | Al-Dhahir, N. |
تاريخ الإتاحة | 2023-04-04T09:09:07Z |
تاريخ النشر | 2022 |
اسم المنشور | IEEE Transactions on Network Science and Engineering |
المصدر | Scopus |
الملخص | The Coronavirus disease 2019 (COVID-19) outbreak continues to significantly expose the vulnerabilities of healthcare systems around the world. These unprecedented circumstances create an opportunity for improving healthcare services which is desperately needed. This paper proposes a novel framework that distributes the patients across heterogeneous medical facilities (MFs) so that a weighted sum of the expected service time (EST) and service time tail probability (STTP) for all patients is minimized. We propose a model-based and model-free algorithms to schedule patients requests across the MFs. Our algorithms prioritize the patients with severe/critical conditions over others who can tolerate more delay in service. Based on the model-based approach, we formulate an optimization problem as a convex combination of both EST and STTP metrics, and apply an efficient iterative algorithm to solve it. Then, a more practical model-free scheme is proposed by adopting a deep reinforcement learning approach. Our model-free approach does not rely on pre-defined models or assumptions about the environment. Rather, it learns to choose scheduling decisions solely through observations of the resulting performance of past decisions. Our extensive results demonstrate a significant performance improvement of our proposed scheduling schemes when compared with other algorithms and competitive baselines. 2013 IEEE. |
اللغة | en |
الناشر | IEEE |
الموضوع | Healthcare Model-Free Reinforcement learning Scheduling Service Time |
النوع | Article |
الصفحات | 622-633 |
رقم العدد | 2 |
رقم المجلد | 9 |
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