Virtualized Care Systems, Medical Artificial Intelligence, and Real-Time Clinical Monitoring in COVID-19 Diagnosis, Screening, Surveillance, and Prevention

M. Walters
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引用次数: 5

Abstract

(Alimadadi et al., 2020) In clinical settings, Internet of Medical Things optimizes patient-centric undertakings with remote patient monitoring, and, in clinical trials, accurately tracks vital signs, blood-sugar levels, and weight trends. (Usak et al., 2020) Internet of Things-assisted cloud-based health monitoring systems deploy heterogeneous physiological and environmental signals to supply contextual data through artificial intelligence-based diagnostic algorithms. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, AIR, Amwell, Ericsson ConsumerLab, Ginger, Kyruus, PwC, and Syneos Health, we performed analyses and made estimates regarding how connected wearable biomedical devices can assist in configuring precise diagnoses. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.
虚拟化医疗系统、医疗人工智能和COVID-19诊断、筛查、监测和预防的实时临床监测
(Alimadadi et al., 2020)在临床环境中,医疗物联网通过远程监测患者,优化以患者为中心的事业,在临床试验中,准确跟踪生命体征、血糖水平和体重趋势。(Usak et al., 2020)物联网辅助的基于云的健康监测系统部署异构生理和环境信号,通过基于人工智能的诊断算法提供上下文数据。通过从埃森哲(Accenture)、AIR、Amwell、爱立信消费者实验室(Ericsson ConsumerLab)、Ginger、Kyruus、普华永道(PwC)和Syneos Health收集的数据,我们对联网的可穿戴生物医学设备如何帮助配置精确诊断进行了分析和估计。研究设计、调查方法和材料访谈是在线进行的,数据采用人口普查局美国社区调查的五个变量(年龄、种族/民族、性别、教育程度和地理区域)加权,以可靠和准确地反映美国的人口构成。
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