P. Bhattad, A. Goyal, Ashley N. Hamati, Akshat Madhok, Shobi Venkatachalam, Divya Sree Madhuramthakam, Vinay Jain, Clinical
{"title":"Internet of Things-enabled Smart Devices in Medical Practice: Healthcare Big Data, Wearable Biometric Sensors, and Real-Time Patient Monitoring","authors":"P. Bhattad, A. Goyal, Ashley N. Hamati, Akshat Madhok, Shobi Venkatachalam, Divya Sree Madhuramthakam, Vinay Jain, Clinical","doi":"10.22381/ajmr7120204","DOIUrl":"https://doi.org/10.22381/ajmr7120204","url":null,"abstract":"","PeriodicalId":91446,"journal":{"name":"American journal of medical research (New York, N.Y.)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48662581","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}
(Rathore et al., 2020) With the advancement of Internet of Thingsbased smart healthcare systems and cloud computing, inexpensive health services and associated support, coherent regulation of the centralized administration (Lăzăroiu et al., 2021), and public health monitoring can be carried out. 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. (Ismail et al., 2020) Real-time remote monitoring applications, through Internet of Things-based medical implants and wearable devices, can decrease clinical visits and hospital care. (Santagati et al., 2020) Internet of Medical Things articulates the networked infrastructure of smart healthcare devices and software applications, ensuring data storage on cloud platforms and leading to accurate diagnoses while preventing and tracking chronic illnesses.
(Rathore et al.， 2020)随着基于物联网的智能医疗保健系统和云计算的进步，廉价的卫生服务和相关支持，集中管理的一致监管(l等，2021)和公共卫生监测可以进行。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。(Ismail et al.， 2020)通过基于物联网的医疗植入物和可穿戴设备，实时远程监控应用可以减少临床就诊和医院护理。(Santagati et al.， 2020)医疗物联网阐明了智能医疗设备和软件应用的网络化基础设施，确保数据存储在云平台上，并在预防和跟踪慢性疾病的同时实现准确诊断。
{"title":"Cognitive Internet of Medical Things, Big Healthcare Data Analytics, and Artificial intelligence-based Diagnostic Algorithms during the COVID-19 Pandemic","authors":"Michael Lăzăroiu George Morrison","doi":"10.22381/ajmr8220212","DOIUrl":"https://doi.org/10.22381/ajmr8220212","url":null,"abstract":"(Rathore et al., 2020) With the advancement of Internet of Thingsbased smart healthcare systems and cloud computing, inexpensive health services and associated support, coherent regulation of the centralized administration (Lăzăroiu et al., 2021), and public health monitoring can be carried out. 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. (Ismail et al., 2020) Real-time remote monitoring applications, through Internet of Things-based medical implants and wearable devices, can decrease clinical visits and hospital care. (Santagati et al., 2020) Internet of Medical Things articulates the networked infrastructure of smart healthcare devices and software applications, ensuring data storage on cloud platforms and leading to accurate diagnoses while preventing and tracking chronic illnesses.","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":"68352877","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}
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 The COVID-19 experience has led to increased awareness of telehealth amongst healthcare providers and patients so as to decrease the risk of transmission and facilitate remote care by use of Internet of Things-enabled smart devices (Krenitsky et al , 2020) Virtual urgent care screening, COVID-19-related remote monitoring for suspected or confirmed patients, incessant supervision wirelessly to decrease workforce risk and use of personal protective equipment, and the progressive shift of outpatient care to telehealth can be harnessed as a reaction to COVID-19 (Moreno et al , 2020) Telehealth can swiftly leverage massive volumes of providers, enable triage so that frontline medical staff working with COVID-19 patients are not overpowered physically with new presentations, furnish clinical services when emergency rooms are overcrowded or not equipped to satisfy demand, and cut down the risk of communicable diseases
{"title":"Internet of Things-enabled Smart Devices, Healthcare Body Sensor Networks, and Online Patient Engagement in COVID-19 Prevention, Screening, and Treatment","authors":"K. Mitchell","doi":"10.22381/ajmr8120213","DOIUrl":"https://doi.org/10.22381/ajmr8120213","url":null,"abstract":"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 The COVID-19 experience has led to increased awareness of telehealth amongst healthcare providers and patients so as to decrease the risk of transmission and facilitate remote care by use of Internet of Things-enabled smart devices (Krenitsky et al , 2020) Virtual urgent care screening, COVID-19-related remote monitoring for suspected or confirmed patients, incessant supervision wirelessly to decrease workforce risk and use of personal protective equipment, and the progressive shift of outpatient care to telehealth can be harnessed as a reaction to COVID-19 (Moreno et al , 2020) Telehealth can swiftly leverage massive volumes of providers, enable triage so that frontline medical staff working with COVID-19 patients are not overpowered physically with new presentations, furnish clinical services when emergency rooms are overcrowded or not equipped to satisfy demand, and cut down the risk of communicable diseases","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":"68351848","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}
(Annis et al , 2020) 2 Conceptual Framework and Literature Review Groundbreaking technologies can be deployed to enhance access to services and delivery of care, in addition to decreasing unsatisfied mental health needs, especially for rural and mainly inadequately serviced communities throughout the COVID-19 outbreak 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 Remote monitoring can harmonize with in-person diagnostic assessment, and track progressing health status by use of medical Internet of Things-based healthcare systems (Hirko et al , 2020) 6 Conclusions and Implications Enlarging training sets and advancing predictive models encompassing preexistent risk factors can supply a full-scale tool driving the decisions of the telehealth providers by use of computer screening algorithms and wearable biometric sensors for COVID-19, with the aim of configuring personalized clinical care
(Annis et al, 2020) 2概念框架和文献综述除了减少未得到满足的心理健康需求外，还可以采用突破性技术来增加获得服务和提供护理的机会。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采用在线进行，数据采用5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，研究参与者被清楚地告知他们在任何时候选择退出研究的自由，而无需提供这样做的理由。扩大训练集和推进包含预先存在的风险因素的预测模型可以提供一个全面的工具，通过使用计算机筛选算法和可穿戴生物识别传感器来驱动远程医疗提供者的决策，目的是配置个性化的临床护理
{"title":"Medical Internet of Things-based Healthcare Systems, Wearable Biometric Sensors, and Personalized Clinical Care in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients","authors":"V. Morgan","doi":"10.22381/ajmr8120218","DOIUrl":"https://doi.org/10.22381/ajmr8120218","url":null,"abstract":"(Annis et al , 2020) 2 Conceptual Framework and Literature Review Groundbreaking technologies can be deployed to enhance access to services and delivery of care, in addition to decreasing unsatisfied mental health needs, especially for rural and mainly inadequately serviced communities throughout the COVID-19 outbreak 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 Remote monitoring can harmonize with in-person diagnostic assessment, and track progressing health status by use of medical Internet of Things-based healthcare systems (Hirko et al , 2020) 6 Conclusions and Implications Enlarging training sets and advancing predictive models encompassing preexistent risk factors can supply a full-scale tool driving the decisions of the telehealth providers by use of computer screening algorithms and wearable biometric sensors for COVID-19, with the aim of configuring personalized clinical care","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":"68352383","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}
Keywords: COVID-19;telemedicine;medical big data;health monitoring system 1 Introduction Virtual care tools such as vital sign monitoring and devices to improve the remote visit physical examination, in addition to home laboratory testing should be networked so as to contain 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 (Rahman et al , 2020) Automated screening algorithms can be developed throughout the intake process, and epidemiologic data should be deployed to regularize examination and practice patterns by use of smart Internet of Things-enabled mobile-based health monitoring systems and medical big data in COVID-19 telemedicine (Madigan et al , 2020) 6 Conclusions and Implications On-demand telehealth can develop into a low-barrier proposal to screening patients for COVID-19, discouraging them from visiting healthcare facilities and thus decreasing physical contact and frontline medical staff use of personal protective equipment
{"title":"Smart Internet of Things-enabled Mobile-based Health Monitoring Systems and Medical Big Data in COVID-19 Telemedicine","authors":"Daniel Kolencik Juraj Cug Juraj Carter","doi":"10.22381/ajmr8120212","DOIUrl":"https://doi.org/10.22381/ajmr8120212","url":null,"abstract":"Keywords: COVID-19;telemedicine;medical big data;health monitoring system 1 Introduction Virtual care tools such as vital sign monitoring and devices to improve the remote visit physical examination, in addition to home laboratory testing should be networked so as to contain 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 (Rahman et al , 2020) Automated screening algorithms can be developed throughout the intake process, and epidemiologic data should be deployed to regularize examination and practice patterns by use of smart Internet of Things-enabled mobile-based health monitoring systems and medical big data in COVID-19 telemedicine (Madigan et al , 2020) 6 Conclusions and Implications On-demand telehealth can develop into a low-barrier proposal to screening patients for COVID-19, discouraging them from visiting healthcare facilities and thus decreasing physical contact and frontline medical staff use of personal protective equipment","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":"68351785","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}
(Mochari-Greenberger and Pande, 2021) 3 Methodology and Empirical Analysis The data used for this study was obtained and replicated from previous research conducted by Accenture, Amwell, Black Book Market Research, Canada Health Infoway, Deloitte, Doximity, Ericsson ConsumerLab, KPMG, Leger, R2G, Syneos Health, PwC, and Sage Growth Partners 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 Virtual patient care can hinder the patient-provider connection, level of physical checkup, coherence of healthcare delivery, and quality of care (Al-khafajiy et al , 2019) As virtual access to high-risk settings across COVID-19 intensive care units can be performed without requiring personal protective equipment, telehealth will increase the provision of critical supplies while ensuring suitable medical personnel by use of wearable Internet of Things healthcare systems
(Mochari-Greenberger and Pande, 2021) 3方法论和实证分析本研究使用的数据来自埃森哲、Amwell、黑本市场研究、加拿大健康信息之路、德勤、Doximity、爱立信消费者实验室、毕马威、Leger、R2G、Syneos Health、普华永道、4 .调查方法和材料访谈采用在线方式进行，数据以5个变量(年龄、种族/民族、性别、教育程度、性别、年龄、年龄、年龄和年龄)加权。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研究参与者的人口构成，他们被清楚地告知他们在任何时候选择退出研究的自由，而无需提供这样做的理由。5结果和讨论虚拟患者护理可能会阻碍患者与提供者的联系，身体检查水平，医疗保健服务的一致性。和护理质量(al -khafajiy等人，2019)由于无需个人防护设备即可在COVID-19重症监护病房的高风险环境中进行虚拟访问，远程医疗将增加关键物资的供应，同时通过使用可穿戴物联网医疗系统确保合适的医务人员
{"title":"Wearable Internet of Things Healthcare Systems, Virtual Care, and Real-Time Clinical Monitoring in Assessing and Treating Patients with COVID-19 Symptoms","authors":"L. Bailey","doi":"10.22381/ajmr8120219","DOIUrl":"https://doi.org/10.22381/ajmr8120219","url":null,"abstract":"(Mochari-Greenberger and Pande, 2021) 3 Methodology and Empirical Analysis The data used for this study was obtained and replicated from previous research conducted by Accenture, Amwell, Black Book Market Research, Canada Health Infoway, Deloitte, Doximity, Ericsson ConsumerLab, KPMG, Leger, R2G, Syneos Health, PwC, and Sage Growth Partners 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 Virtual patient care can hinder the patient-provider connection, level of physical checkup, coherence of healthcare delivery, and quality of care (Al-khafajiy et al , 2019) As virtual access to high-risk settings across COVID-19 intensive care units can be performed without requiring personal protective equipment, telehealth will increase the provision of critical supplies while ensuring suitable medical personnel by use of wearable Internet of Things healthcare 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":"68352254","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}
Building our argument by drawing on data collected from Accenture, GlobalWebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Introduction The extensive data of COVID-19 patients can be assimilated and inspected by cutting-edge machine learning algorithms to grasp the pattern of viral transmission, optimize diagnostic swiftness and precision, advance adequate therapeutic methods, and identify the most vulnerable individuals according to personalized genetic and physiological features. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Global-WebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients' vital signs configuring clinical data in pervasive mobile patient-centric healthcare. 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.
{"title":"Artificial Intelligence-enabled Wearable Medical Devices, Clinical and Diagnostic Decision Support Systems, and Internet of Things-based Healthcare Applications in COVID-19 Prevention, Screening, and Treatment","authors":"R. Barnes","doi":"10.22381/ajmr8220211","DOIUrl":"https://doi.org/10.22381/ajmr8220211","url":null,"abstract":"Building our argument by drawing on data collected from Accenture, GlobalWebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Introduction The extensive data of COVID-19 patients can be assimilated and inspected by cutting-edge machine learning algorithms to grasp the pattern of viral transmission, optimize diagnostic swiftness and precision, advance adequate therapeutic methods, and identify the most vulnerable individuals according to personalized genetic and physiological features. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Global-WebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients' vital signs configuring clinical data in pervasive mobile patient-centric healthcare. 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":"68352393","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 digital epidemiological surveillance, smart telemedicine diagnosis systems, and machine learning-based real-time data sensing and processing in COVID-19 remote patient monitoring, and building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. 4.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. (Jiang et al., 2020) The efficient deployment and utilization of data fusion (Lăzăroiu and Harrison, 2021) enable accurate evaluation in remote patient monitoring, optimizing preventive care for chronic diseases by use of machine learning-based automated diagnostic systems and artificial intelligence-enabled wearable medical devices.
采用最新的研究成果，涵盖数字流行病学监测、智能远程医疗诊断系统和基于机器学习的COVID-19远程患者监测实时数据传感和处理，并利用从埃森哲、Amwell、黑本市场研究、CMA、CFPC、德勤、HBR、Kyruus、普华永道、RCPSC、Sage Growth Partners和索尼收集的数据来构建我们的论点，我们对远程患者监护中的机器学习算法和深度神经网络驱动的物联网进行了分析和估计。通过利用从埃森哲、Amwell、黑本市场研究、CMA、CFPC、德勤、HBR、Kyruus、普华永道、RCPSC、Sage Growth Partners和索尼收集的数据来构建我们的论点，我们对远程患者监测中的机器学习算法和深度神经网络驱动的物联网进行了分析和估计。在适当情况下，对已完成调查的汇编数据进行了描述性统计。4.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。(Jiang et al.， 2020)数据融合的有效部署和利用(l z roiu和Harrison, 2021)可以通过使用基于机器学习的自动诊断系统和支持人工智能的可穿戴医疗设备，在远程患者监测中进行准确评估，优化慢性病的预防保健。
{"title":"Digital Epidemiological Surveillance, Smart Telemedicine Diagnosis Systems, and Machine Learning-based Real-Time Data Sensing and Processing in COVID-19 Remote Patient Monitoring","authors":"Mark Miklencicova Renata Woods","doi":"10.22381/ajmr8220215","DOIUrl":"https://doi.org/10.22381/ajmr8220215","url":null,"abstract":"Employing recent research results covering digital epidemiological surveillance, smart telemedicine diagnosis systems, and machine learning-based real-time data sensing and processing in COVID-19 remote patient monitoring, and building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. 4.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. (Jiang et al., 2020) The efficient deployment and utilization of data fusion (Lăzăroiu and Harrison, 2021) enable accurate evaluation in remote patient monitoring, optimizing preventive care for chronic diseases by use of machine learning-based automated diagnostic systems and artificial intelligence-enabled wearable medical 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":"68353072","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}
Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Deloitte, Ericsson ConsumerLab, Kyruus, The Rockefeller Foundation, Syneos Health, and USAID, we performed analyses and made estimates regarding artificial intelligence-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. 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. Results and Discussion Artificial intelligence-enabled wearable medical devices for preliminary disease detection and monitoring and physiochemical alterations assist in medical diagnosis, assessing infection levels and subsequent therapeutic decision through artificial intelligence-driven biosensors. (Jaleel et al., 2020) Deep machine learning and cloud computing are pivotal in Internet of Things-based healthcare by enabling data analytics-based smart medical services (Lăzăroiu et al., 2021) in evidence-based decision making, remote monitoring, disease prevention and diagnoses, and risk factor identification.
我们利用从埃森哲、安姆威尔、德勤、爱立信消费者实验室、Kyruus、洛克菲勒基金会、Syneos Health和美国国际开发署收集的数据，对2019冠状病毒病大流行期间人工智能驱动的生物传感器在诊断、监测和预防方面的应用进行了分析和估计。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。人工智能支持的可穿戴医疗设备用于疾病的初步检测和监测，以及通过人工智能驱动的生物传感器进行物理化学改变，协助医学诊断，评估感染水平和随后的治疗决策。(Jaleel et al.， 2020)深度机器学习和云计算通过在循证决策、远程监测、疾病预防和诊断以及风险因素识别方面实现基于数据分析的智能医疗服务(l z等人，2021)，在基于物联网的医疗保健中发挥关键作用。
{"title":"Medical Big Data and Wearable Internet of Things Healthcare Systems in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients","authors":"Deborah Hurley","doi":"10.22381/ajmr8220216","DOIUrl":"https://doi.org/10.22381/ajmr8220216","url":null,"abstract":"Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Deloitte, Ericsson ConsumerLab, Kyruus, The Rockefeller Foundation, Syneos Health, and USAID, we performed analyses and made estimates regarding artificial intelligence-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. 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. Results and Discussion Artificial intelligence-enabled wearable medical devices for preliminary disease detection and monitoring and physiochemical alterations assist in medical diagnosis, assessing infection levels and subsequent therapeutic decision through artificial intelligence-driven biosensors. (Jaleel et al., 2020) Deep machine learning and cloud computing are pivotal in Internet of Things-based healthcare by enabling data analytics-based smart medical services (Lăzăroiu et al., 2021) in evidence-based decision making, remote monitoring, disease prevention and diagnoses, and risk factor identification.","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":"68353082","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}
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
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
{"title":"Smart Biomedical Sensors, Big Healthcare Data Analytics, and Virtual Care Technologies in Monitoring, Detection, and Prevention of COVID-19","authors":"Kevin Morris","doi":"10.22381/ajmr8120216","DOIUrl":"https://doi.org/10.22381/ajmr8120216","url":null,"abstract":"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","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":"68352058","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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