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Map reduce for optimizing a large-scale dynamic network - the Internet of hearts.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This study introduces a faster method for analyzing patient data in the Internet of Heart (IoH) system. The new approach enables smart healthcare by efficiently recognizing disease patterns in large patient networks.

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    Area of Science:

    • Biomedical Engineering
    • Computer Science
    • Health Informatics

    Background:

    • Smart and connected healthcare relies heavily on data analytics.
    • The Internet of Heart (IoH) system dynamically networks patients based on condition similarity.
    • Real-time disease pattern recognition is challenging in large-scale IoH networks.

    Purpose of the Study:

    • To develop a novel, efficient scheme for optimizing the IoH network.
    • To enable timely disease pattern recognition in large patient populations.
    • To improve the computational efficiency of connected healthcare systems.

    Main Methods:

    • A parallel, distributed network optimization scheme was developed.
    • Stochastic gradient descent embeds patients with similar conditions into local networks.
    • Local networks are optimally combined into a global network for parallel processing.

    Main Results:

    • The proposed scheme significantly enhances computational efficiency.
    • It enables the IoH system to handle large-scale patient data.
    • Achieved an 80-fold speedup compared to conventional algorithms for 20,000 patients.

    Conclusions:

    • The developed scheme is effective and efficient for large-scale IoH systems.
    • It facilitates smart connected healthcare through timely disease pattern recognition.
    • This advancement supports early disease detection and intervention in connected care settings.