Towards an efficient and Energy-Aware mobile big health data architecture

Navaz, Alramzana Nujum; Serhani, Mohamed Adel; Al-Qirim, Nabeel; Gergely, Marton

doi:10.1016/j.cmpb.2018.10.008

Cited by 17 publications

(18 citation statements)

References 22 publications

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“…This theme captures the application of technology to meet the responsibilities of healthcare through e-Health (Shao et al 2016) and m-Health (Bravo et al 2018) services. This theme also encompasses related issues, such as disease (Amirian et al 2017); e-Health networks (Shao et al 2016); monitoring and alerting system (Manogaran et al 2018); m-Health services (Bravo et al 2018;Navaz et al 2018;Li et al 2015;Wu et al 2016, Wu et al 2017; privacy and security issues (Patil and Seshadri 2014) surveillance (Amirian et al 2017) and alert systems (Manogaran et al 2018). Shao et al (2016) developed an algorithm based on a reduced variable neighbourhood search to improve the functioning of e-Health networks.…”

Section: Applications Of Bda In Healthcarementioning

confidence: 99%

“…This theme acknowledges how BDA has improved decision-making processes in healthcare organisations with evidence-based decisions and faster decision making (Navaz et al 2018); it also shows how improved design produced better public health policy at the national level (Christensen et al 2018). suggested that system trust could be preserved by a cloud design wherein tools are decoupled from data stores and interfaces.…”

Section: Decision Supportmentioning

confidence: 99%

“…suggested that system trust could be preserved by a cloud design wherein tools are decoupled from data stores and interfaces. Navaz et al (2018) proposed that efficient analytics could optimise the handling, processing, and analysis of health data from mobile devices. Christensen et al (2019) identified that the duration of device use influences the physical activity level of patients.…”

Section: Decision Supportmentioning

confidence: 99%

“…Increased adoption of newer technologies make real-time data from sensors in electronic devices available to healthcare functions. These data are often accumulated from health devices (Gopal et al 2019;Ma et al 2018), Internet of Things (IoT) devices (Bravo et al 2018;George, Chacko, and Kurien 2019), and smartphone applications (Navaz et al 2018;Wu et al 2017). In general, body temperature sensors, blood oxygen sensors, and electrocardiogram sensors, among other sensors are attached to a patient's body to monitor health parameters continuously.…”

Section: Sensor Datamentioning

confidence: 99%

See 3 more Smart Citations

Big data analytics in healthcare: a systematic literature review

Khanra

Dhir

Islam

et al. 2020

Enterprise Information Systems

162

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The current study performs a systematic literature review (SLR) to synthesise prior research on the applicability of big data analytics (BDA) in healthcare. The SLR examines the outcomes of 41 studies, and presents them in a comprehensive framework. The findings from this study suggest that applications of BDA in healthcare can be observed from five perspectives, namely, health awareness among the general public, interactions among stakeholders in the healthcare ecosystem, hospital management practices, treatment of specific medical conditions, and technology in healthcare service delivery. This SLR recommends actionable future research agendas for scholars and valuable implications for theory and practice.

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Section: Applications Of Bda In Healthcarementioning

confidence: 99%

Section: Decision Supportmentioning

confidence: 99%

Section: Decision Supportmentioning

confidence: 99%

Section: Sensor Datamentioning

confidence: 99%

See 2 more Smart Citations

Big data analytics in healthcare: a systematic literature review

Khanra

Dhir

Islam

et al. 2020

Enterprise Information Systems

162

View full text Add to dashboard Cite

show abstract

“…Real-time display is also required to be automatically customized according to the visualizing device's screen size and even the device battery level. These challenges were addressed in [281], in which special dashboard functionalities were integrated with visualization features, such as zoom-in and zoom-out, and filtering.…”

Section: Challenges Related To Visualizationmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

Self Cite

216

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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Challenges and opportunities of big data analytics in healthcare

Goyal,

Malviya

2023

Health Care Science

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Data science is an interdisciplinary discipline that employs big data, machine learning algorithms, data mining techniques, and scientific methodologies to extract insights and information from massive amounts of structured and unstructured data. The healthcare industry constantly creates large, important databases on patient demographics, treatment plans, results of medical exams, insurance coverage, and more. The data that IoT (Internet of Things) devices collect is of interest to data scientists. Data science can help with the healthcare industry's massive amounts of disparate, structured, and unstructured data by processing, managing, analyzing, and integrating it. To get reliable findings from this data, proper management and analysis are essential. This article provides a comprehensive study and discussion of process data analysis as it pertains to healthcare applications. The article discusses the advantages and disadvantages of using big data analytics (BDA) in the medical industry. The insights offered by BDA, which can also aid in making strategic decisions, can assist the healthcare system.

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Towards an efficient and Energy-Aware mobile big health data architecture

Cited by 17 publications

References 22 publications

Big data analytics in healthcare: a systematic literature review

Big data analytics in healthcare: a systematic literature review

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Challenges and opportunities of big data analytics in healthcare

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