Big Data in home healthcare: A new frontier in personalized medicine. Medical emergency services and prediction of hypertension risks

Clim, Antonio; Zota, Răzvan Daniel; Tinică, Grigore

doi:10.1080/20479700.2018.1548158

Cited by 26 publications

(9 citation statements)

References 25 publications

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“…With the development of Internet cloud computing and other information technologies, smart healthcare and big data are widely utilized in transportation, banking, medical, and education industries [4][5][6]. Intelligent medical big data is a collection of data information in the medical industry.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Huang¹,

Dang

2022

Journal of Healthcare Engineering

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This study explores the risk factors of chronic pulmonary heart disease (CPHD) induced by plateau chronic obstructive pulmonary disease (COPD) based on intelligent medical treatment and big data of electrocardiogram (ECG) signal. Based on GPU, a wavelet algorithm is introduced to extract features of ECG signal, and it was combined with generalized regression neural network (GRNN) to improve classification accuracy. From June 2018 to December 2020, 10,185 patients diagnosed with COPD in the plateau area by pulmonary function testing, ECG, and chest X-ray at X Hospital are taken as the research objects to evaluate the distribution of CPHD incidence at different ages and altitudes. The running time of GTX780Ti is about 15 times shorter than that of CPU. The accuracy of N detection based on the GPU-accelerated neural network model reached 98.06%. Accuracy (Acc), sensitivity (Se), specificity (Sp), and positive rate (PR) of V were 99.03%, 89.17%, 98.92%, and 93.18%, respectively. The Acc, Se, Sp, and PR of S were 99.54%, 86.22%, 99.74%, and 92.56%, respectively. The GRNN classification accuracy was up to 98%. 19% of COPD patients were diagnosed with CPHD, including 1,409 males (72.82%) and 526 females (36.24%). The highest prevalence of CPHD was 64.60% when the altitude was 1,900–2,499 m, and the prevalence was only 2.43% when the altitude was ≥3,500 m. The highest prevalence of CPHD was 63.77% at the age of 61–70 years, and the lowest prevalence at the age of 15∼20 years was only 0.26%. Therefore, the GPU-based neural network model improved the classification accuracy of ECG signals. Age and altitude were risk factors for CPHD induced by high-altitude COPD, which provided a reference for the prevention, diagnosis, and treatment of CPHD in high-altitude areas.

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Section: Introductionmentioning

confidence: 99%

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Huang¹,

Dang

2022

Journal of Healthcare Engineering

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“…They are pulled from diverse sources and are stored in centralized IT systems that make managing and sharing a challenging task. Requesting, sending, receiving, and compiling patient data are time-consuming and require excessive resources [1]. Proper management and safe retrieval of these data enable healthcare systems to create holistic views of patients, improve the quality of care and treatments, enhance communication, and improve health outcomes [2].…”

Section: Introductionmentioning

confidence: 99%

Blockchain technology in healthcare: Challenges and opportunities

Attaran

2020

International Journal of Healthcare Management

237

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Patients and healthcare practitioners are faced with the challenge of accessing, managing, integrating, and sharing health records securely. Patients should be able to manage their health records anywhere in the world, keep track of medical background, give access to data, and share those with any healthcare professional securely. Direct access to data for patients and a more robust data-sharing infrastructure could better prepare the healthcare system to manage public health threats during the emergence of deadly disease outbreak such as COVID-19. Current technologies in use by the healthcare industry do not adequately address these requirements due to limitations related to privacy, security, and full ecosystem interoperability. This paper conducted a literature review to find out the pivotal roles blockchain technology play in solving some of the most critical and challenging issues facing the healthcare industry. This paper identifies challenges and opportunities for implementing blockchain technology in healthcare and summarizes health-related blockchain products and key players offering solutions across different applications. In doing this, our research extends and complements existing blockchain research in healthcare.

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“…This is further facilitated by the fact that the number of sensors used in those devices is growing rapidly: In addition to accelerometers and gyroscopes to track physical activity, sensors to measure physiological signals such as heart rate, body temperature, respiratory rate, and blood oxygen saturation, that may correspond to health/sickness state of the human body [11], are now frequently found in wearables as well [12], [13]. Consequently, even outside the realms of personal science wearables, have long been seen as promising tools to facilitate health-related monitoring and enable personalized medicine [14], [15], and have been proposed or used to monitor conditions as diverse as cardiovascular disease [16], [17], Alzheimer's [18] and graft-versus-host disease [19].…”

Section: Introductionmentioning

confidence: 99%

Quantified Flu: an individual-centered approach to gaining sickness-related insights from wearable data

Alexiou³

et al. 2021

Preprint

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Background: Wearables have been used widely for monitoring health in general and recent research results show that they can be used for predicting infections based on physiological symptoms. So far the evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are comprised of people interested in learning about themselves individually using their own data, often gathered via wearable devices. Objective: We explore how a co-creation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system to monitor symptoms of infection alongside wearable sensor data. Methods: We engaged into a co-creation and design process with an existing community of personal science practitioners, jointly developing a working prototype of an online tool to perform symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis, investigating the process of how this prototype was created in a decentralized and iterative fashion. Results: The Quantified Flu prototype allows users to perform daily symptom reporting and is capable of visualizing those symptom reports on a timeline together with the resting heart rate, body temperature and respiratory rate as measured by wearable devices. We observe a high level of engagement, with over half of the 92 users that engaged in the symptom tracking becoming regular users, reporting over three months of data each. Furthermore, our netnographic analysis highlights how the current Quantified Flu prototype is a result of an interactive and continuous co-creation process in which new prototype releases spark further discussions of features and vice versa. Conclusions: As shown by the high level of user engagement and iterative development, an open co-creation process can be successfully used to develop a tool that is tailored to individual needs, decreasing dropout rates.

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Big Data in home healthcare: A new frontier in personalized medicine. Medical emergency services and prediction of hypertension risks

Cited by 26 publications

References 25 publications

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Analysis of Inducing Factors of Chronic Pulmonary Heart Disease Caused by Chronic Obstructive Pulmonary Disease at High Altitude through Epidemiological Investigation under Intelligent Medicine and Big Data

Blockchain technology in healthcare: Challenges and opportunities

Quantified Flu: an individual-centered approach to gaining sickness-related insights from wearable data

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