(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。
{"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":"https://doi.org/10.22381/ajmr8220218","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":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68352700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transcranial Magnetic Stimulation (TMS) in Treatment Resistant Depression (TRD): The First Quarter Century","authors":"","doi":"10.22381/ajmr8120211","DOIUrl":"https://doi.org/10.22381/ajmr8120211","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68352125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
(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.
(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。
{"title":"Virtualized Care Systems, Medical Artificial Intelligence, and Real-Time Clinical Monitoring in COVID-19 Diagnosis, Screening, Surveillance, and Prevention","authors":"M. Walters","doi":"10.22381/ajmr8220213","DOIUrl":"https://doi.org/10.22381/ajmr8220213","url":null,"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.","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68352928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
(Poppas et al , 2020) 2 Conceptual Framework and Literature Review Patients who have progressed most from the increased convenience of telehealth services encounter obstacles leaving the house as a result of chronic illness, proceed along to see a specialist, or reside in an inadequately serviced location with unsatisfactory access to care 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 (Kaplan, 2021) For patients in the process of mental health treatment who are worried about COVID-19 exposure risk, telehealth has enabled uninterruptedness of mental health care (Hirko et al , 2020) Health systems have advanced automated logic flows that transfer moderate-to-high-risk COVID-19 confirmed individuals to nurse triage lines while allowing them to arrange video visits with healthcare providers so as to prevent transit to in-person care settings
(Poppas等人，2020)2概念框架和文献综述从远程医疗服务的日益便利中进步最大的患者由于慢性疾病而遇到离开家的障碍，继续去看专家，4 .调查方法和材料。访谈是在线进行的，使用人口普查局的美国社区调查(American Community Survey)对五个变量(年龄、种族/民族、性别、教育程度和地理区域)对数据进行加权，以可靠、准确地反映美国的人口构成(Kaplan，2021)对于在心理健康治疗过程中担心COVID-19暴露风险的患者，远程医疗已经实现了精神卫生保健的不间断(Hirko等人，2020)卫生系统具有先进的自动化逻辑流程，可将中等至高风险的COVID-19确诊患者转移到护理分流线上，同时允许他们安排与医疗保健提供者的视频访问，以防止转移到现场护理环境
{"title":"Virtualized Care Systems, Wearable Sensor-based Devices, and Real-Time Medical Data Analytics in COVID-19 Patient Health Prediction","authors":"Rebecca S Parker","doi":"10.22381/ajmr8120215","DOIUrl":"https://doi.org/10.22381/ajmr8120215","url":null,"abstract":"(Poppas et al , 2020) 2 Conceptual Framework and Literature Review Patients who have progressed most from the increased convenience of telehealth services encounter obstacles leaving the house as a result of chronic illness, proceed along to see a specialist, or reside in an inadequately serviced location with unsatisfactory access to care 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 (Kaplan, 2021) For patients in the process of mental health treatment who are worried about COVID-19 exposure risk, telehealth has enabled uninterruptedness of mental health care (Hirko et al , 2020) Health systems have advanced automated logic flows that transfer moderate-to-high-risk COVID-19 confirmed individuals to nurse triage lines while allowing them to arrange video visits with healthcare providers so as to prevent transit to in-person care settings","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital epidemiological surveillance in monitoring, detection, and prevention of COVID-19 is optimized by use of medical artificial intelligence, clinical and diagnostic decision support systems, machine learning-based real-time data sensing and processing, and smart healthcare devices and applications. 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. (Pustokhina et al., 2020) Body sensor networks integrate interconnected bio-sensors and wearable healthcare devices (Kovacova and Lăzăroiu, 2021;Lyons and Lăzăroiu, 2020) that assess abnormal alterations in vital physiological signs and share medical imaging data for patient diagnosis and monitoring, being instrumental in chronic diseases by use of deep learning-based applications. Conclusions, Implications, Limitations, and Further Research Directions Artificial intelligence-enabled wearable medical devices, virtualized care systems, and wireless biomedical sensing devices are pivotal in COVID-19 screening, testing, and treatment.
通过使用医疗人工智能、临床和诊断决策支持系统、基于机器学习的实时数据传感和处理以及智能医疗设备和应用程序，优化了COVID-19监测、检测和预防中的数字流行病学监测。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。(Pustokhina et al.， 2020)身体传感器网络整合了相互连接的生物传感器和可穿戴医疗设备(Kovacova和l z roiu, 2021;Lyons和l z roiu, 2020)，评估重要生理体征的异常变化，共享医学成像数据，用于患者诊断和监测，通过使用基于深度学习的应用程序，有助于慢性疾病。人工智能支持的可穿戴医疗设备、虚拟化医疗系统和无线生物医学传感设备在COVID-19筛查、测试和治疗中至关重要。
{"title":"Virtual Care Technologies, Wearable Health Monitoring Sensors, and Internet of Medical Things-based Smart Disease Surveillance Systems in the Diagnosis and Treatment of COVID-19 Patients","authors":"S. Maxwell","doi":"10.22381/ajmr8220219","DOIUrl":"https://doi.org/10.22381/ajmr8220219","url":null,"abstract":"Digital epidemiological surveillance in monitoring, detection, and prevention of COVID-19 is optimized by use of medical artificial intelligence, clinical and diagnostic decision support systems, machine learning-based real-time data sensing and processing, and smart healthcare devices and applications. 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. (Pustokhina et al., 2020) Body sensor networks integrate interconnected bio-sensors and wearable healthcare devices (Kovacova and Lăzăroiu, 2021;Lyons and Lăzăroiu, 2020) that assess abnormal alterations in vital physiological signs and share medical imaging data for patient diagnosis and monitoring, being instrumental in chronic diseases by use of deep learning-based applications. Conclusions, Implications, Limitations, and Further Research Directions Artificial intelligence-enabled wearable medical devices, virtualized care systems, and wireless biomedical sensing devices are pivotal in COVID-19 screening, testing, and treatment.","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68352751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Wearable Internet of Medical Things Sensor Devices, Big Healthcare Data, and Artificial Intelligence-based Diagnostic Algorithms in Real-Time COVID-19 Detection and Monitoring Systems","authors":"","doi":"10.22381/ajmr82202110","DOIUrl":"https://doi.org/10.22381/ajmr82202110","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68352793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Employing recent research results covering smart telemedicine diagnosis systems, biomedical big data, and telehealth outpatient monitoring in COVID19 screening, testing, and treatment, and building my argument by drawing on data collected from Accenture, Amwell, Brookings, GlobalWebIndex, KPMG, PwC, The Rockefeller Foundation, Syneos Health, and USAID, I performed analyses and made estimates regarding how telemedicine and telehealth technologies can be used in inpatient and outpatient video visits 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 Study participants were informed clearly about their freedom to opt out of the study at any point of time without providing justification for doing so 5 Results and Discussion As the volume of confirmed COVID-19 patients and of asymptomatic patients with infection increases, by advancing telehealth, medical personnel are protected from exposure to such a contagious virus, while personal protective equipment can be conserved when unavailabilities take place (Rosen et al , 2020) Home monitoring systems integrated in electronic health records enable frontline medical staff to enroll, triage, and monitor COVID-19 patients remotely by harnessing reported outcome measures
{"title":"Smart Telemedicine Diagnosis Systems, Biomedical Big Data, and Telehealth Outpatient Monitoring in COVID-19 Screening, Testing, and Treatment","authors":"Kenneth Campbell","doi":"10.22381/ajmr81202110","DOIUrl":"https://doi.org/10.22381/ajmr81202110","url":null,"abstract":"Employing recent research results covering smart telemedicine diagnosis systems, biomedical big data, and telehealth outpatient monitoring in COVID19 screening, testing, and treatment, and building my argument by drawing on data collected from Accenture, Amwell, Brookings, GlobalWebIndex, KPMG, PwC, The Rockefeller Foundation, Syneos Health, and USAID, I performed analyses and made estimates regarding how telemedicine and telehealth technologies can be used in inpatient and outpatient video visits 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 Study participants were informed clearly about their freedom to opt out of the study at any point of time without providing justification for doing so 5 Results and Discussion As the volume of confirmed COVID-19 patients and of asymptomatic patients with infection increases, by advancing telehealth, medical personnel are protected from exposure to such a contagious virus, while personal protective equipment can be conserved when unavailabilities take place (Rosen et al , 2020) Home monitoring systems integrated in electronic health records enable frontline medical staff to enroll, triage, and monitor COVID-19 patients remotely by harnessing reported outcome measures","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
(Mann et al , 2020) 2 Conceptual Framework and Literature Review Computationally streamlined, extremely secured algorithms can protect electronic health records harnessed instantaneously for telediagnosis associated with Internet of Things-based healthcare systems in the remote treatment of patients during the COVID-19 pandemic 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 (Abdel-Basset et al , 2021) Smart healthcare can decrease the transmission of COVID-19, enhance the safety of frontline medical staff, boost efficacy by declining the severity of such a contagious disease on confirmed patients, and reduce mortality rates by use of wearable Internet of Medical Things systems, e-health applications, ambient sensors (digital surveillance), and remote diagnostics (Goldschmidt, 2020) Medical centers are reacting to COVID-19 by swiftly embracing telemedicine and virtual care that provide digital or remote healthcare services by using data-driven tools and technologies for treatment of confirmed patients in a safe, accessible, and appropriate manner
(Mann et al, 2020) 2概念框架和文献综述计算简化，在COVID-19大流行期间，与基于物联网的医疗保健系统相关的远程诊断相关的即时电子健康记录在适当的时候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在线进行，数据由五个变量(年龄、种族/民族、性别、教育程度、(Abdel-Basset et al, 2021)智能医疗可以减少COVID-19的传播，增强一线医务人员的安全性，通过降低确诊患者这种传染病的严重程度来提高疗效，并通过使用可穿戴式医疗物联网系统、电子健康应用程序、医疗中心通过迅速采用远程医疗和虚拟医疗来应对COVID-19，通过使用数据驱动的工具和技术，以安全、可获取和适当的方式治疗确诊患者，提供数字或远程医疗服务
{"title":"Artificial Intelligence-enabled Healthcare Delivery and Digital Epidemiological Surveillance in the Remote Treatment of Patients during the COVID-19 Pandemic","authors":"A. Phillips","doi":"10.22381/ajmr8120214","DOIUrl":"https://doi.org/10.22381/ajmr8120214","url":null,"abstract":"(Mann et al , 2020) 2 Conceptual Framework and Literature Review Computationally streamlined, extremely secured algorithms can protect electronic health records harnessed instantaneously for telediagnosis associated with Internet of Things-based healthcare systems in the remote treatment of patients during the COVID-19 pandemic 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 (Abdel-Basset et al , 2021) Smart healthcare can decrease the transmission of COVID-19, enhance the safety of frontline medical staff, boost efficacy by declining the severity of such a contagious disease on confirmed patients, and reduce mortality rates by use of wearable Internet of Medical Things systems, e-health applications, ambient sensors (digital surveillance), and remote diagnostics (Goldschmidt, 2020) Medical centers are reacting to COVID-19 by swiftly embracing telemedicine and virtual care that provide digital or remote healthcare services by using data-driven tools and technologies for treatment of confirmed patients in a safe, accessible, and appropriate manner","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68351928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Empirical evidence on artificial intelligence-powered diagnostic tools, networked medical devices, and cyber-physical healthcare systems in assessing and treating patients with COVID-19 symptoms has been scarcely documented in the literature. (Tsikala Vafea et al., 2020) Internet of Medical Things necessitates the deployment of health data from wearable mobile healthcare and smart sensing devices and applications networked across electronic health records in clinical and diagnostic decision support and remote healthcare systems. (Williams Samuel et al., 2020) COVID-19 detection and monitoring systems can acquire instantaneous symptom data from artificial intelligence-enabled wearable medical devices, identifying potential COVID-19 cases by use of machine learning algorithms. 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.
人工智能驱动的诊断工具、联网医疗设备和网络物理医疗系统在评估和治疗COVID-19患者症状方面的经验证据在文献中几乎没有记录。(Tsikala Vafea et al.， 2020)医疗物联网需要部署来自可穿戴移动医疗保健和智能传感设备的健康数据，以及临床和诊断决策支持以及远程医疗保健系统中电子健康记录联网的应用程序。(Williams Samuel et al.， 2020) COVID-19检测和监测系统可以从支持人工智能的可穿戴医疗设备获取即时症状数据，通过使用机器学习算法识别潜在的COVID-19病例。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。
{"title":"Artificial Intelligence-Powered Diagnostic Tools, Networked Medical Devices, and Cyber-Physical Healthcare Systems in Assessing and Treating Patients with COVID-19 Symptoms","authors":"Helen Michalikova Katarina Frajtova Welch","doi":"10.22381/ajmr8220217","DOIUrl":"https://doi.org/10.22381/ajmr8220217","url":null,"abstract":"Empirical evidence on artificial intelligence-powered diagnostic tools, networked medical devices, and cyber-physical healthcare systems in assessing and treating patients with COVID-19 symptoms has been scarcely documented in the literature. (Tsikala Vafea et al., 2020) Internet of Medical Things necessitates the deployment of health data from wearable mobile healthcare and smart sensing devices and applications networked across electronic health records in clinical and diagnostic decision support and remote healthcare systems. (Williams Samuel et al., 2020) COVID-19 detection and monitoring systems can acquire instantaneous symptom data from artificial intelligence-enabled wearable medical devices, identifying potential COVID-19 cases by use of machine learning algorithms. 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.","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68352634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
(Usak et al., 2020) Cloud and wireless sensor networks (Lăzăroiu et al., 2021) harnessed in data processing and storage (Andronie et al., 2021a, b) can ensure monitoring rehabilitation and recovery processes by analyzing health status and behavioral changes. 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. (Khan and Algarni, 2020) The advancement of smart and computerized molecular diagnostic tools harnessing biomedical big data analysis, cloud computing, and machine learning-based real-time data sensing and processing (Kovacova and Lăzăroiu, 2021) can assist in COVID-19 detection, monitoring, and treatment, and cloud data storage for supportive decisions. Conclusions, Implications, Limitations, and Further Research Directions Internet of Medical Things assists smart healthcare systems in analyzing gathered data, integrating wearable health monitoring sensors, diagnostics tools, and telemedicine equipment during the COVID-19 pandemic by use of wireless biomedical sensing devices.
在数据处理和存储(Andronie等人，2021a, b)中利用云和无线传感器网络(l等人，2021)可以通过分析健康状况和行为变化来确保监测康复和恢复过程。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。利用生物医学大数据分析、云计算和基于机器学习的实时数据传感和处理的智能和计算机化分子诊断工具的进步(Kovacova和l z roiu, 2021)可以协助COVID-19的检测、监测和治疗，并为支持性决策提供云数据存储。在2019冠状病毒病大流行期间，医疗物联网通过使用无线生物医学传感设备，协助智能医疗系统分析收集的数据，集成可穿戴健康监测传感器、诊断工具和远程医疗设备。
{"title":"Internet of Things-based Smart Healthcare Systems and Wireless Biomedical Sensing Devices in Monitoring, Detection, and Prevention of COVID-19","authors":"Anna Riley","doi":"10.22381/ajmr8220214","DOIUrl":"https://doi.org/10.22381/ajmr8220214","url":null,"abstract":"(Usak et al., 2020) Cloud and wireless sensor networks (Lăzăroiu et al., 2021) harnessed in data processing and storage (Andronie et al., 2021a, b) can ensure monitoring rehabilitation and recovery processes by analyzing health status and behavioral changes. 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. (Khan and Algarni, 2020) The advancement of smart and computerized molecular diagnostic tools harnessing biomedical big data analysis, cloud computing, and machine learning-based real-time data sensing and processing (Kovacova and Lăzăroiu, 2021) can assist in COVID-19 detection, monitoring, and treatment, and cloud data storage for supportive decisions. Conclusions, Implications, Limitations, and Further Research Directions Internet of Medical Things assists smart healthcare systems in analyzing gathered data, integrating wearable health monitoring sensors, diagnostics tools, and telemedicine equipment during the COVID-19 pandemic by use of wireless biomedical sensing devices.","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68353030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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