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المؤلفAl-Abbasi, A. O.
المؤلفSamara, L.
المؤلفSalem, S.
المؤلفHamila, R.
المؤلفAl-Dhahir, N.
تاريخ الإتاحة2023-04-04T09:09:07Z
تاريخ النشر2022
اسم المنشورIEEE Transactions on Network Science and Engineering
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/TNSE.2021.3127942
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41609
الملخصThe 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.
اللغةen
الناشرIEEE
الموضوعHealthcare
Model-Free
Reinforcement learning
Scheduling
Service Time
العنوانA Unified Framework for Differentiated Services in Intelligent Healthcare Systems
النوعArticle
الصفحات622-633
رقم العدد2
رقم المجلد9
dc.accessType Abstract Only


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