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

Clicks: 174
ID: 103972
2020
Article Quality & Performance Metrics
Overall Quality Improving Quality
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Combines engagement data with AI-assessed academic quality
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Abstract
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. Keywords: COVID-19, Internet of Things, Cloud plus terminal, Intelligent assistance, Quality control
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bai2020chineseclinical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bai, Li;Yang, Dawei;Wang, Xun;Tong, Lin;Zhu, Xiaodan;Zhong, Nanshan;Bai, Chunxue;Powell, Charles A.;Chen, Rongchang;Zhou, Jian;Song, Yuanlin;Zhou, Xin;Zhu, Huili;Han, Baohui;Li, Qiang;Shi, Guochao;Li, Shengqing;Wang, Changhui;Qiu, Zhongmin;Zhang, Yong;Xu, Yu;Liu, Jie;Zhang, Ding;Wu, Chaomin;Li, Jing;Yu, Jinming;Wang, Jiwei;Dong, Chunling;Wang, Yaoli;Wang, Qi;Zhang, Lichuan;Zhang, Min;Ma, Xia;Zhao, Lin;Yu, Wencheng;Xu, Tao;Jin, Yang;Wang, Xiongbiao;Wang, Yuehong;Jiang, Yan;Chen, Hong;Xiao, Kui;Zhang, Xiaoju;Song, Zhenju;Zhang, Ziqiang;Wu, Xueling;Sun, Jiayuan;Shen, Yao;Ye, Maosong;Tu, Chunlin;Jiang, Jinjun;Yu, Hai;Tan, Fei;
Journal clinical ehealth
Year 2020
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