Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Ningrong Lei, Murtadha Kareem, Seung Ki Moon, Edward J. Ciaccio, U Rajendra Acharya and Oliver Faust
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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
Oliver Faust: College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK

IJERPH, 2021, vol. 18, issue 2, 1-19

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.

Keywords: human and AI collaboration; medical diagnosis support; deep learning; symbiotic analysis process; human controlled machine work (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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