American journal of medical research (New York, N.Y.)最新文献_第4页

Smart Healthcare Devices and Applications, Machine Learning-based Automated Diagnostic Systems, and Real-Time Medical Data Analytics in COVID-19 Screening, Testing, and Treatment 2019冠状病毒病筛查、测试和治疗中的智能医疗设备和应用程序、基于机器学习的自动诊断系统和实时医疗数据分析

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220218

Ann Kucera Jiri Stanley

{"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}

引用次数: 7

Transcranial Magnetic Stimulation (TMS) in Treatment Resistant Depression (TRD): The First Quarter Century 经颅磁刺激(TMS)治疗难治性抑郁症(TRD):前25年

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120211

引用次数: 2

Virtualized Care Systems, Medical Artificial Intelligence, and Real-Time Clinical Monitoring in COVID-19 Diagnosis, Screening, Surveillance, and Prevention 虚拟化医疗系统、医疗人工智能和COVID-19诊断、筛查、监测和预防的实时临床监测

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220213

M. Walters

{"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}

引用次数: 5

Virtualized Care Systems, Wearable Sensor-based Devices, and Real-Time Medical Data Analytics in COVID-19 Patient Health Prediction 虚拟化医疗系统、基于可穿戴传感器的设备和实时医疗数据分析在COVID-19患者健康预测中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120215

Rebecca S Parker

{"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}

引用次数: 1

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 虚拟医疗技术、可穿戴式健康监测传感器和基于医疗物联网的智能疾病监测系统在COVID-19患者诊断和治疗中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220219

S. Maxwell

{"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}

引用次数: 9

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 可穿戴医疗物联网传感器设备、医疗大数据和基于人工智能的诊断算法在COVID-19实时检测和监测系统中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr82202110

引用次数: 9

Smart Telemedicine Diagnosis Systems, Biomedical Big Data, and Telehealth Outpatient Monitoring in COVID-19 Screening, Testing, and Treatment 智能远程医疗诊断系统、生物医学大数据和远程医疗门诊监测在COVID-19筛查、检测和治疗中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr81202110

Kenneth Campbell

{"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}

引用次数: 1

Artificial Intelligence-enabled Healthcare Delivery and Digital Epidemiological Surveillance in the Remote Treatment of Patients during the COVID-19 Pandemic COVID-19大流行期间患者远程治疗中的人工智能医疗保健服务和数字流行病学监测

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8120214

A. Phillips

{"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}

引用次数: 4

Artificial Intelligence-Powered Diagnostic Tools, Networked Medical Devices, and Cyber-Physical Healthcare Systems in Assessing and Treating Patients with COVID-19 Symptoms 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症状患者中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220217

Helen Michalikova Katarina Frajtova Welch

{"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}

引用次数: 2

Internet of Things-based Smart Healthcare Systems and Wireless Biomedical Sensing Devices in Monitoring, Detection, and Prevention of COVID-19 基于物联网的智能医疗系统和无线生物医学传感设备在COVID-19监测、检测和预防中的应用

American journal of medical research (New York, N.Y.) Pub Date : 2021-01-01 DOI: 10.22381/ajmr8220214

Anna Riley

{"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}

引用次数: 13