Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Lei, Ningrong; Kareem, Murtadha; Moon, Seung Ki; Ciaccio, Edward J.; Acharya, U. Rajendra; Faust, Oliver

doi:10.3390/ijerph18020813

Cited by 9 publications

(5 citation statements)

References 49 publications

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“…The top five most productive nations, according by the number of original papers produced, are shown in descending order in Table 5, for convenience in interpreting Figure 6. Of the total number of papers, the United States contributed 83, followed by those from the People's Republic of China (78), England (35), Australia (26), and Japan (22).…”

Section: Int J Environ Res Public Health 2023 20 3930mentioning

confidence: 99%

“…In the healthcare sector, IoT can enable the collection and analysis of large amounts of health data from various sources, such as wearable devices, electronic health records, and environmental sensors. Big data can be used to analyze and interpret these data, which can lead to improved health outcomes and more efficient healthcare delivery [78][79][80]. ix.…”

Section: Novel Research Frontiers Of Big Data Epidemic Control For Fu...mentioning

confidence: 99%

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Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

Lai

Akram

et al. 2023

IJERPH

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In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, “COVID-19 infection” was the hottest cluster with 31 references in 2020, while “Internet of things (IoT) platform and unified health algorithm” was the emerging research topic with 15 citations. “Influenza, internet, China, human mobility, and province” were the emerging keywords in the year 2021–2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

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Section: Int J Environ Res Public Health 2023 20 3930mentioning

confidence: 99%

Section: Novel Research Frontiers Of Big Data Epidemic Control For Fu...mentioning

confidence: 99%

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

Lai

Akram

et al. 2023

IJERPH

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“…The atrial fibrillation detection service validation tool automates the comparison between cardiologist and deep learning analysis results [3]. Furthermore, relevant signal graphs allow the users to inspect the analysis results which might lead to a deeper understanding of artificial intelligence for atrial fibrillation detection.…”

Section: Impact Reviewmentioning

confidence: 99%

“…Prevalence in the general population and severities of outcome are the main drivers behind that statistic. Stroke refers to the detrimental processes that occur when brain tissue is deprived of oxygen [3]. An Ischemic stroke occurs when debris, such as blood clots and plaque, blocks an artery.…”

Section: Introductionmentioning

confidence: 99%

Atrial fibrillation detection service validation tool

2021

Self Cite

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We developed a software tool to validate a deep learning algorithm for an atrial fibrillation detection service with heart rate data from a clinical study. The deep learning algorithm analyses the measurement data and establishes an estimated atrial fibrillation probability for each heartbeat. The software tool displays both data and deep learning analysis results. Furthermore, the graphical user interface can be used by medical experts to detect atrial fibrillation periods in the data and establish a reference result which will be treated as ground truth in subsequent result analysis steps. Once both deep learning and expert results are available, a confusion matrix is produced and the algorithm performance is validated by establishing accuracy, sensitivity, specificity, and f1-score. The software tool was created in Python and the software incorporated a graphical user interface as well as functional elements for data display and deep learning. To establish the required functionality, we used three different parallel processing methods for: (1) user interface processing, (2) data handling, and (3) deep learning. This highlights the need for parallel processing methods even for projects with a low or midrange complexity. We have learned that the functionality of individual components can be expressed elegantly in Python. However, the lack of parallel debugging support makes it rather difficult to integrate functional components to establish a working solution.

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“…Advancements in wireless technology, artificial intelligence, and sensor technology have enabled the use of remote patient monitoring as a method to prevent and detect medical emergencies [ 1 ]. This includes, for example, cardiovascular diseases [ 2 ], stroke [ 3 ], sepsis [ 4 ], and trauma, including falls, which together claim millions of lives each year [ 5 - 7 ]. These medical emergencies can often be attributed to population aging, as elderly individuals are more susceptible to disease and disability.…”

Section: Introductionmentioning

confidence: 99%

Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Protocol for a Scoping Review

Seth¹,

Jalo²,

Högstedt³

et al. 2022

JMIR Res Protoc

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Background Population growth and aging have highlighted the need for more effective home and prehospital care. Interconnected medical devices and applications, which comprise an infrastructure referred to as the Internet of Medical Things (IoMT), have enabled remote patient monitoring and can be important tools to cope with these demographic changes. However, developing IoMT platforms requires profound knowledge of clinical needs and challenges related to interoperability and how these can be managed with suitable technologies. Objective The purpose of this scoping review is to summarize the best practices and technologies to overcome interoperability concerns in IoMT platform development for medical emergencies in home and prehospital care. Methods This scoping review will be conducted in accordance with Arksey and O’Malley’s 5-stage framework and adhere to the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols) guidelines. Only peer-reviewed articles published in English will be considered. The databases/web search engines that will be used are IEEE Xplore, PubMed, Scopus, Google Scholar, National Center for Biotechnology Information, SAGE Journals, and ScienceDirect. The search process for relevant literature will be divided into 4 different steps. This will ensure that a suitable approach is followed in terms of search terms, limitations, and eligibility criteria. Relevant articles that meet the inclusion criteria will be screened in 2 stages: abstract and title screening and full-text screening. To reduce selection bias, the screening process will be performed by 2 reviewers. Results The results of the preliminary search indicate that there is sufficient literature to form a good foundation for the scoping review. The search was performed in April 2022, and a total of 4579 articles were found. The main clinical focus is the prevention and management of falls, but other medical emergencies, such as heart disease and stroke, are also considered. Preliminary results show that little attention has been given to real-time IoMT platforms that can be deployed in real-world care settings. The final results are expected to be presented in a scoping review in 2023 and will be disseminated through scientific conference presentations, oral presentations, and publication in a peer-reviewed journal. Conclusions This scoping review will provide insights and recommendations regarding how interoperable real-time IoMT platforms can be developed to handle medical emergencies in home and prehospital care. The findings of this research could be used by researchers, clinicians, and implementation teams to facilitate future development and interdisciplinary discussions. International Registered Report Identifier (IRRID) DERR1-10.2196/40243

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Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention

Cited by 9 publications

References 49 publications

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

Atrial fibrillation detection service validation tool

Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Protocol for a Scoping Review

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