Smart Healthcare Devices and Applications, Machine Learning-based Automated Diagnostic Systems, and Real-Time Medical Data Analytics in COVID-19 Screening, Testing, and Treatment
{"title":"Smart Healthcare Devices and Applications, Machine Learning-based Automated Diagnostic Systems, and Real-Time Medical Data Analytics in COVID-19 Screening, Testing, and Treatment","authors":"Ann Kucera Jiri Stanley","doi":"10.22381/ajmr8220218","DOIUrl":null,"url":null,"abstract":"(Zhang and Han, 2020) Real-time patient monitoring and biomedical big data are determining in disease prediction, diagnosis, and support clinical decision by use of artificial intelligence-enabled wearable medical devices and machine learning-based automated diagnostic systems. 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. (Chen et al., 2020) COVID-19 detection and monitoring systems can be put into action throughout an Internet of Medical Things infrastructure, monitoring both potential and confirmed patients in real time, and as regards the treatment responses of recovered individuals, while grasping the nature of the virus by acquiring, inspecting, and archiving valuable data. (Bordel et al., 2020) Internet of Medical Things deploys networked medical devices and wireless communication to facilitate the sharing of healthcare data through artificial intelligence-based diagnostic algorithms, real-time medical data analytics, and machine learning-based automated diagnostic systems.","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American journal of medical research (New York, N.Y.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22381/ajmr8220218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
(Zhang and Han, 2020) Real-time patient monitoring and biomedical big data are determining in disease prediction, diagnosis, and support clinical decision by use of artificial intelligence-enabled wearable medical devices and machine learning-based automated diagnostic systems. 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. (Chen et al., 2020) COVID-19 detection and monitoring systems can be put into action throughout an Internet of Medical Things infrastructure, monitoring both potential and confirmed patients in real time, and as regards the treatment responses of recovered individuals, while grasping the nature of the virus by acquiring, inspecting, and archiving valuable data. (Bordel et al., 2020) Internet of Medical Things deploys networked medical devices and wireless communication to facilitate the sharing of healthcare data through artificial intelligence-based diagnostic algorithms, real-time medical data analytics, and machine learning-based automated diagnostic systems.
(Zhang and Han, 2020)通过使用支持人工智能的可穿戴医疗设备和基于机器学习的自动诊断系统,实时患者监测和生物医学大数据在疾病预测、诊断和支持临床决策方面发挥着重要作用。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。(Chen et al., 2020) COVID-19检测和监测系统可以在整个医疗物联网基础设施中投入使用,实时监测潜在患者和确诊患者,以及康复个体的治疗反应,同时通过获取、检查和存档有价值的数据来掌握病毒的性质。(Bordel et al., 2020)医疗物联网部署联网医疗设备和无线通信,通过基于人工智能的诊断算法、实时医疗数据分析和基于机器学习的自动诊断系统,促进医疗数据的共享。