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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm

Kun Cao, Yangguang Cui, Liying Li

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
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    PubMed
    Summary
    This summary is machine-generated.

    This study optimizes digital healthcare applications on edge devices using machine learning and swarm intelligence. The new method improves average quality-of-service (QoS) by 15.7% and balances individual application QoS by 64.3%.

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    Area of Science:

    • Computer Science
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Medical cyber-physical systems (MCPS) are increasingly deployed in digital healthcare, utilizing CPU-GPU cooperative multiprocessor system-on-chips (MPSoCs) on edge devices.
    • Existing workload estimation methods for these systems are often pessimistic and neglect personalized application needs and hardware reliability, leading to potential failures and degraded quality-of-service (QoS).

    Purpose of the Study:

    • To explore CPU-GPU cooperative QoS optimization for personalized digital healthcare applications on reliable edge devices.
    • To address limitations in current workload estimation and scheduling for medical edge devices.

    Main Methods:

    • Developed a machine learning-based predictor for accurate application workload estimation.
    • Created a feature-driven predictor for application QoS estimation.
    • Integrated predictors into a swarm intelligent application scheduling scheme using a cooperative dual-population evolutionary algorithm (c-DPEA) for optimal mapping and partitioning.

    Main Results:

    • Augmented the average QoS of digital healthcare applications by 15.7%.
    • Balanced the QoS of individual digital healthcare applications by 64.3%.
    • Demonstrated improved performance and reliability for medical edge devices.

    Conclusions:

    • The proposed machine learning and swarm intelligence approach effectively optimizes CPU-GPU cooperative QoS for personalized digital healthcare applications.
    • The solution enhances both overall and individual application QoS while ensuring reliability of edge devices.
    • This work contributes to more robust and efficient digital healthcare systems.