Elements of Healthcare Big Data Analytics

Mehta, Nishita; Pandit, Anil; Kulkarni, Meenal

doi:10.1007/978-3-030-31672-3_2

Cited by 16 publications

(18 citation statements)

References 17 publications

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“…Content may change prior to final publication. [57], [58], [59], [25], [60] [61], [62], [63], [64] [65], [66], [67], [68], [69] [70], [71], [72] demands in less-developed countries without fully open healthcare systems [74]. Nowadays, a wide range of mobile devices of various sizes, from cell phones and smartwatches to small phones and tablets, along with integrated wearables, implants, and location-based trackers and sensors are available.…”

Section: B Technologies Supporting Schmentioning

confidence: 99%

“…Care providers may need to make critical decisions about the health of their patients using these huge volumes of health information provided in a multitude of data formats. State-of-the-art healthcare systems with IoT and big data analytics are proposed in [53] , [66] , [67] . The key elements of big data analytics are the data elements, functional elements, human elements, and security elements [66] .…”

Section: Smart and Connected Health: Review Classification And Comparisonmentioning

confidence: 99%

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Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

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Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

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Section: B Technologies Supporting Schmentioning

confidence: 99%

Section: Smart and Connected Health: Review Classification And Comparisonmentioning

confidence: 99%

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

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“…The size of the data must be decreased and made available to anybody interested. This not only aids in prompt patient care, but it also enhances the sensitivity and reliability of the underlying application [105,106]. Furthermore, the patient's existing medications are monitored, and the risk of a new drug is assessed for potential adverse reactions [107,108].…”

Section: Iot Machine Learning Big Data Analysis In Healthcare Systemmentioning

confidence: 99%

A Systematic Analysis of Internet of Things in Healthcare Applications : A Review

Arora¹,

Singh²

2021

JUSST

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The Internet of Things (IoT) is a network of physical devices, software, and hardware that communicate with one another. As the population ages, healthcare resources become scarce, and medical expenses rise, IoT-based solutions must be adapted to meet these issues in healthcare. To enhance the monitoring efficiency of the IoT-based healthcare system, several studies have been conducted. In this paper, the architecture utilized in the IoT, particularly cloud-integrated systems and security in IoT devices is explored. Factors like accuracy and power consumption are major concerns in the Internet of Things, therefore research projects aimed at enhancing the performance of IoT-based healthcare systems are highlighted. In this work, data management strategies in an IoT-based healthcare system with cloud capabilities are thoroughly examined. The performance of the IoT-based healthcare system is examined, as well as its benefits and drawbacks. Moreover, a comparative analysis is also done on some existing technologies that are utilized in healthcare. It has been observed from past studies that IoT protocol such as 6LoWPAN is mostly utilized in the domain of health care. The majority of research studies are effective in detecting many symptoms and accurately predicting illnesses. The IoT-based healthcare system built specifically for the elderly is an effective way to keep track of their medical concerns. High power consumption, a scarcity of resources, and security concerns major drawbacks of current systems are included in the proposed study.

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“…Big data applications are successfully used in many areas such as banking, insurance, health, education, spatial data, and the detection of online customer behavior [26][27][28][29][30][31][32]. Tourism sector is one of the sectors where customer accumulations are found to be important.…”

Section: Introductionmentioning

confidence: 99%

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

2020

Turk J Elec Eng & Comp Sci

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Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor to gather data from visitors of the hotels and the analysis was supported with an optimization approach through the deep autoencoder network. According to the data density gathered from the structure developed with distributed computing and the API software in C# language, server group numbers were increased to list the most affordable hotel in the desired times. Thanks to the developed architecture and software, response times of the servers were significantly reduced. In detail, it was seen that the HAProxy responded 11 times faster than NetScaler as the new architecture responded 1160 times faster than the old one. Also, the HashSet system in the newly developed architecture responded 18 times faster than the List system and as general, the new architecture was found to be 9 times faster than the old architecture.

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Elements of Healthcare Big Data Analytics

Cited by 16 publications

References 17 publications

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

A Systematic Analysis of Internet of Things in Healthcare Applications : A Review

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

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