Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

LEI, Ningrong, KAREEM, Murtadha, MOON, Seung Ki, CIACCIO, Edward J, ACHARYA, U Rajendra and FAUST, Oliver (2021). Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention. International Journal of Environmental Research and Public Health, 18 (2), p. 813.

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Open Access URL: https://www.mdpi.com/1660-4601/18/2/813/htm (Published version)
Link to published version:: https://doi.org/10.3390/ijerph18020813
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    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.

    Item Type: Article
    Uncontrolled Keywords: Toxicology
    Identification Number: https://doi.org/10.3390/ijerph18020813
    Page Range: p. 813
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 22 Jan 2021 10:05
    Last Modified: 17 Mar 2021 16:16
    URI: http://shura.shu.ac.uk/id/eprint/28008

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