Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19)

The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible..

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:3

Enthalten in:

Clinical eHealth - 3(2020), Seite 7-15

Sprache:

Englisch

Beteiligte Personen:

Li Bai [VerfasserIn]
Dawei Yang [VerfasserIn]
Xun Wang [VerfasserIn]
Lin Tong [VerfasserIn]
Xiaodan Zhu [VerfasserIn]
Nanshan Zhong [VerfasserIn]
Chunxue Bai [VerfasserIn]
Charles A. Powell [VerfasserIn]
Rongchang Chen [VerfasserIn]
Jian Zhou [VerfasserIn]
Yuanlin Song [VerfasserIn]
Xin Zhou [VerfasserIn]
Huili Zhu [VerfasserIn]
Baohui Han [VerfasserIn]
Qiang Li [VerfasserIn]
Guochao Shi [VerfasserIn]
Shengqing Li [VerfasserIn]
Changhui Wang [VerfasserIn]
Zhongmin Qiu [VerfasserIn]
Yong Zhang [VerfasserIn]
Yu Xu [VerfasserIn]
Jie Liu [VerfasserIn]
Ding Zhang [VerfasserIn]
Chaomin Wu [VerfasserIn]
Jing Li [VerfasserIn]
Jinming Yu [VerfasserIn]
Jiwei Wang [VerfasserIn]
Chunling Dong [VerfasserIn]
Yaoli Wang [VerfasserIn]
Qi Wang [VerfasserIn]
Lichuan Zhang [VerfasserIn]
Min Zhang [VerfasserIn]
Xia Ma [VerfasserIn]
Lin Zhao [VerfasserIn]
Wencheng Yu [VerfasserIn]
Tao Xu [VerfasserIn]
Yang Jin [VerfasserIn]
Xiongbiao Wang [VerfasserIn]
Yuehong Wang [VerfasserIn]
Yan Jiang [VerfasserIn]
Hong Chen [VerfasserIn]
Kui Xiao [VerfasserIn]
Xiaoju Zhang [VerfasserIn]
Zhenju Song [VerfasserIn]
Ziqiang Zhang [VerfasserIn]
Xueling Wu [VerfasserIn]
Jiayuan Sun [VerfasserIn]
Yao Shen [VerfasserIn]
Maosong Ye [VerfasserIn]
Chunlin Tu [VerfasserIn]
Jinjun Jiang [VerfasserIn]
Hai Yu [VerfasserIn]
Fei Tan [VerfasserIn]

Links:

doi.org [kostenfrei]
doaj.org [kostenfrei]
www.sciencedirect.com [kostenfrei]
Journal toc [kostenfrei]

Themen:

COVID-19
Cloud plus terminal
Intelligent assistance
Internet of Things
Medicine
Quality control
R

doi:

10.1016/j.ceh.2020.03.001

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

DOAJ001008986