Hybrid decision support to monitor atrial fibrillation for stroke prevention
Article
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). https://doi.org/10.3390/ijerph18020813
Article Title | Hybrid decision support to monitor atrial fibrillation for stroke prevention |
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ERA Journal ID | 44293 |
Article Category | Article |
Authors | Lei, Ningrong, Kareem, Murtadha, Moon, Seung Ki, Ciaccio, Edward J., Acharya, U Rajendra and Faust, Oliver |
Journal Title | International Journal of Environmental Research and Public Health |
Journal Citation | 18 (2) |
Article Number | 813 |
Number of Pages | 19 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1660-4601 |
1661-7827 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph18020813 |
Web Address (URL) | https://www.mdpi.com/1660-4601/18/2/813 |
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 | Deep learning; human and AI collaboration; medical diagnosis support; symbiotic analysis process; human controlled machine work |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Sheffield Hallam University, United Kingdom |
Nanyang Technological University, Singapore | |
Columbia University, United States | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
School of Management and Enterprise |
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https://research.usq.edu.au/item/z1vx0/hybrid-decision-support-to-monitor-atrial-fibrillation-for-stroke-prevention
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