智能生物医学传感器、大医疗数据分析和虚拟医疗技术用于监测、检测和预防COVID-19

Kevin Morris
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引用次数: 5

摘要

关键词:在医疗大数据分析和智能生物医学传感器的支持下,人工智能驱动的系统可以提供有关各地区资源部署的信息,并利用虚拟医疗技术为COVID-19大流行期间的系统重新部署和临床医生参与提供建议(Wittenberg et al .;2021) 2概念框架和文献综述对于未感染COVID-19的患者,特别是有重大感染风险的人(例如,有既往病史的老年人),远程医疗可以提供随时可用的标准护理,而无需在过度拥挤的设施或医疗实践等候室中暴露。在适当情况下,对已完成调查的汇编数据进行描述性统计。4调查方法和材料。访谈是在线进行的,数据由五个变量(年龄、种族/民族、性别、教育程度、和地理区域),使用人口普查局的美国社区调查来可靠和准确地反映美国的人口构成(Kumar等人,2021)。通过利用生物信息学方法先进的简化预测模型和算法来识别和检查包括临床大数据在内的广泛的各种数据集,医疗物联网可以与临床实践相结合。利用疾病风险预测和预后,进一步实现个体化医疗
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Smart Biomedical Sensors, Big Healthcare Data Analytics, and Virtual Care Technologies in Monitoring, Detection, and Prevention of COVID-19
Keywords: COVID-19;big healthcare data analytics;virtual care technology 1 Introduction Fortified by big healthcare data analytics and smart biomedical sensors, artificial intelligence-powered systems can supply information as regards resource deployment in various regions, offering suggestions on system redeployment and clinician involvement during the COVID-19 pandemic by use of virtual care technologies (Wittenberg et al , 2021) 2 Conceptual Framework and Literature Review For patients not infected with COVID-19, particularly persons at significant risk of being affected (e g , older individuals having prior medical conditions), telehealth can deliver readily available access to standard care without exposure in an overcrowded facility or in medical practice waiting rooms Descriptive statistics of compiled data from the completed surveys were calculated when appropriate 4 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 (Kumar et al , 2021) Internet of Medical Things can be integrated with clinical practice by leveraging streamlined predictive models and algorithms advanced by use of approaches of bioinformatics to identify and inspect wide-ranging various datasets, comprising clinical big data, to harness disease-risk forecast and prognosis to further personalized medicine
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