American journal of medical research (New York, N.Y.)最新文献_第3页

Internet of Things-enabled Smart Devices in Medical Practice: Healthcare Big Data, Wearable Biometric Sensors, and Real-Time Patient Monitoring 医疗实践中支持物联网的智能设备:医疗大数据、可穿戴生物识别传感器和实时患者监测

American journal of medical research (New York, N.Y.) Pub Date : 2021-02-19 DOI: 10.22381/ajmr7120204

P. Bhattad, A. Goyal, Ashley N. Hamati, Akshat Madhok, Shobi Venkatachalam, Divya Sree Madhuramthakam, Vinay Jain, Clinical

引用次数: 4

Cognitive Internet of Medical Things, Big Healthcare Data Analytics, and Artificial intelligence-based Diagnostic Algorithms during the COVID-19 Pandemic 2019冠状病毒病大流行期间的认知医疗物联网、医疗大数据分析和基于人工智能的诊断算法

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220212

Michael Lăzăroiu George Morrison

引用次数: 10

Internet of Things-enabled Smart Devices, Healthcare Body Sensor Networks, and Online Patient Engagement in COVID-19 Prevention, Screening, and Treatment 支持物联网的智能设备、医疗保健身体传感器网络以及在线患者参与COVID-19预防、筛查和治疗

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120213

K. Mitchell

{"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}

引用次数: 8

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 基于医疗物联网的医疗保健系统、可穿戴生物识别传感器和个性化临床护理，用于远程监测和护理确诊或疑似COVID-19患者

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120218

V. Morgan

{"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}

引用次数: 7

Smart Internet of Things-enabled Mobile-based Health Monitoring Systems and Medical Big Data in COVID-19 Telemedicine 基于智能物联网的移动健康监测系统和COVID-19远程医疗中的医疗大数据

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120212

Daniel Kolencik Juraj Cug Juraj Carter

{"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}

引用次数: 10

Wearable Internet of Things Healthcare Systems, Virtual Care, and Real-Time Clinical Monitoring in Assessing and Treating Patients with COVID-19 Symptoms 可穿戴物联网医疗系统、虚拟医疗和实时临床监测在COVID-19患者症状评估和治疗中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120219

L. Bailey

{"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}

引用次数: 6

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 支持人工智能的可穿戴医疗设备、临床和诊断决策支持系统以及基于物联网的医疗保健应用，用于COVID-19的预防、筛查和治疗

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220211

R. Barnes

{"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}

引用次数: 11

Digital Epidemiological Surveillance, Smart Telemedicine Diagnosis Systems, and Machine Learning-based Real-Time Data Sensing and Processing in COVID-19 Remote Patient Monitoring 数字流行病学监测，智能远程医疗诊断系统，以及基于机器学习的COVID-19患者远程监测实时数据传感和处理

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220215

Mark Miklencicova Renata Woods

{"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}

引用次数: 6

Medical Big Data and Wearable Internet of Things Healthcare Systems in Remotely Monitoring and Caring for Confirmed or Suspected COVID-19 Patients 医疗大数据和可穿戴物联网医疗系统在COVID-19确诊或疑似患者远程监测和护理中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220216

Deborah Hurley

{"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}

引用次数: 14

Smart Biomedical Sensors, Big Healthcare Data Analytics, and Virtual Care Technologies in Monitoring, Detection, and Prevention of COVID-19 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

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120216

Kevin Morris

{"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}

引用次数: 5