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Management for stroke intelligent early warning empowered by big data.
Chen, Xiaoyong; Yang, Boxiong; Zhao, Shuai; Wei, Wei; Chen, Jialu; Ding, Jie; Wang, Hong; Sun, Peng; Gan, Lin.
  • Chen X; School of Health Industry Management, University of Sanya, Sanya, China.
  • Yang B; School of Information & Intelligence Engineering, University of Sanya, Sanya, China.
  • Zhao S; School of Health Industry Management, University of Sanya, Sanya, China.
  • Wei W; School of Health Industry Management, University of Sanya, Sanya, China.
  • Chen J; Sanya Traditional Chinese Medicine Hospital, Sanya, China.
  • Ding J; Wound Healing Research Group, Department of Surgery, Faculty of Medicine and Dentistry University of Alberta, Canada.
  • Wang H; Swiss TCM Clinic, Zurich, Switzerland.
  • Sun P; Sanya People's Hospital, Sanya, China.
  • Gan L; School of Information & Intelligence Engineering, University of Sanya, Sanya, China.
Comput Electr Eng ; 106: 108602, 2023 Mar.
Article in English | MEDLINE | ID: covidwho-2228825
ABSTRACT
Global aging population, especially with the global pandemic outbreak of the Corona Virus Disease 2019 (COVID-19), has endangered human health security. Digital information technology through big data empowerment and intelligent application is widely considered a key element to solve the problems. Stroke is a life-threaten disorder. We studied individual health management and disease risk perception using human health assessment model and make full use of wearable wireless sensor, Internet of Things, big data, and Artificial Intelligence for potential risk monitoring and real-time stroke warning. We proposed an effective method of monitoring, early warning and rescue to improve the stroke treatment. The result shows that the health management empowered by big data can generate new opportunities and ideas to solve early detection and warning of stroke.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Electr Eng Year: 2023 Document Type: Article Affiliation country: J.compeleceng.2023.108602

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Comput Electr Eng Year: 2023 Document Type: Article Affiliation country: J.compeleceng.2023.108602