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Article

Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

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College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
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Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK
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School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA
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Ngee Ann Polytechnic, Singapore 598269, Singapore
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Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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School of Management and Enterprise, University of Southern Queensland, Toowoomba 4350, Australia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(2), 813; https://doi.org/10.3390/ijerph18020813
Received: 8 December 2020 / Revised: 8 January 2021 / Accepted: 11 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Human and AI Collaborative Decision Making in Healthcare)
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. View Full-Text
Keywords: human and AI collaboration; medical diagnosis support; deep learning; symbiotic analysis process; human controlled machine work human and AI collaboration; medical diagnosis support; deep learning; symbiotic analysis process; human controlled machine work
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MDPI and ACS Style

Lei, N.; Kareem, M.; Moon, S.K.; Ciaccio, E.J.; Acharya, U.R.; Faust, O. Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention. Int. J. Environ. Res. Public Health 2021, 18, 813. https://doi.org/10.3390/ijerph18020813

AMA Style

Lei N, Kareem M, Moon SK, Ciaccio EJ, Acharya UR, Faust O. Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention. International Journal of Environmental Research and Public Health. 2021; 18(2):813. https://doi.org/10.3390/ijerph18020813

Chicago/Turabian Style

Lei, Ningrong, Murtadha Kareem, Seung K. Moon, Edward J. Ciaccio, U R. Acharya, and Oliver Faust. 2021. "Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention" International Journal of Environmental Research and Public Health 18, no. 2: 813. https://doi.org/10.3390/ijerph18020813

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