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Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Author

Listed:
  • Ningrong Lei

    (College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Murtadha Kareem

    (Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK)

  • Seung Ki Moon

    (School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore)

  • Edward J. Ciaccio

    (Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA)

  • U Rajendra Acharya

    (Ngee Ann Polytechnic, Singapore 598269, Singapore
    Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
    School of Management and Enterprise, University of Southern Queensland, Toowoomba 4350, Australia)

  • Oliver Faust

    (College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK)

Abstract

In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.

Suggested Citation

  • Ningrong Lei & Murtadha Kareem & Seung Ki Moon & Edward J. Ciaccio & U Rajendra Acharya & Oliver Faust, 2021. "Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention," IJERPH, MDPI, vol. 18(2), pages 1-19, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:813-:d:482834
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    References listed on IDEAS

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    1. Oliver Faust & Edward J. Ciaccio & U. Rajendra Acharya, 2020. "A Review of Atrial Fibrillation Detection Methods as a Service," IJERPH, MDPI, vol. 17(9), pages 1-34, April.
    2. Oliver Faust & Ningrong Lei & Eng Chew & Edward J. Ciaccio & U Rajendra Acharya, 2020. "A Smart Service Platform for Cost Efficient Cardiac Health Monitoring," IJERPH, MDPI, vol. 17(17), pages 1-18, August.
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    Cited by:

    1. Jun Liu & Shuang Lai & Ayesha Akram Rai & Abual Hassan & Ray Tahir Mushtaq, 2023. "Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace," IJERPH, MDPI, vol. 20(5), pages 1-24, February.

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