Impact of big data in healthcare: A survey

Thara, D K; Premasudha, B. G.; Ram, V. Ravi; Suma, R. K.

doi:10.1109/ic3i.2016.7918057

Cited by 17 publications

(4 citation statements)

References 7 publications

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“…ThereareseveraldocumentspublishedsurveycoveringdifferentaspectsofBigDatatechnology. For example, the survey by (Thara, Premasudha, Ram, & Suma, 2016) presents the review of variousresearcheffortsmadeinhealthcaredomainusingBigDataconceptsandmethodologies.In (Wang,Kung,&Byrd,2018),theauthorsexaminethehistoricaldevelopment,architecturaldesign andcomponentfunctionalitiesofbigdataanalytics.In(Dogaru&Dumitrache,2017),theauthors presentbigdatafromtheperspectiveofimprovinghealthcareservicesandoffersaholisticviewof systemsecurityandfactorsdeterminingsecuritybreaches.Abaselineforassessingtherapidgrowth oftheimplementationofbigdataanalyticsintohealthcareandlifescienceaspectsthatassistsin theunderstandingthebigdataapplicationsanditsimpactspresentedin (Zayeri,2017).In (Moreira, Rodrigues,Furtadoetal.,2018),theauthorsuseofamachinelearningtechnique,knownasaveraged one-dependenceestimators,isproposedforreal-timepregnancydataanalysisfromIoTdevicesand gateways.Theauthors (Sterling,2017)provideanoverviewofbigdataanalyticsforhealthcare.…”

Section: Related Workmentioning

confidence: 99%

Big Data Classification and Internet of Things in Healthcare

Rghioui

Lloret

Oumnad

2020

International Journal of E-Health and Medical Communications

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Every single day, a massive amount of data is generated by different medical data sources. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and data classification. The authors can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it. The fundamental purpose of this article is to summarize the role of Big Data analysis in healthcare, and to provide a comprehensive analysis of the various techniques involved in mining big data. This article provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future works. Therefore, in this article, the use of machine learning techniques is proposed for real-time diabetic patient data analysis from IoT devices and gateways.

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Section: Related Workmentioning

confidence: 99%

Big Data Classification and Internet of Things in Healthcare

Rghioui

Lloret

Oumnad

2020

International Journal of E-Health and Medical Communications

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“…Therefore, in an adaptive solution of the health care data architecture, we must support the diversity and continuity of the authenticators, which are referred to as managed resources that create the elements of authentication [ 17 ]. IoHT data can be large in volume, known as big data, and this comes from the vast number of devices that could be used per patient depending on the service being provided; thus, the data become sensitive information as these are often health-related data that are communicated and stored as electronic health records [ 18 ]. IoHT devices store, transmit, and communicate data in real time, and these data are often used in critical settings to measure the health of a patient for observation, rehabilitation, and recovery, making protected methodologies of passing the data between legitimate entities a necessity [ 19 ].…”

Section: Research Design and Resultsmentioning

confidence: 99%

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

Suleski¹,

Ahmed²

2023

J Med Internet Res

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The health care industry has faced various challenges over the past decade as we move toward a digital future where services and data are available on demand. The systems of interconnected devices, users, data, and working environments are referred to as the Internet of Health Care Things (IoHT). IoHT devices have emerged in the past decade as cost-effective solutions with large scalability capabilities to address the constraints on limited resources. These devices cater to the need for remote health care services outside of physical interactions. However, IoHT security is often overlooked because the devices are quickly deployed and configured as solutions to meet the demands of a heavily saturated industry. During the COVID-19 pandemic, studies have shown that cybercriminals are exploiting the health care industry, and data breaches are targeting user credentials through authentication vulnerabilities. Poor password use and management and the lack of multifactor authentication security posture within IoHT cause a loss of millions according to the IBM reports. Therefore, it is important that health care authentication security moves toward adaptive multifactor authentication (AMFA) to replace the traditional approaches to authentication. We identified a lack of taxonomy for data models that particularly focus on IoHT data architecture to improve the feasibility of AMFA. This viewpoint focuses on identifying key cybersecurity challenges in a theoretical framework for a data model that summarizes the main components of IoHT data. The data are to be used in modalities that are suited for health care users in modern IoHT environments and in response to the COVID-19 pandemic. To establish the data taxonomy, a review of recent IoHT papers was conducted to discuss the related work in IoHT data management and use in next-generation authentication systems. Reports, journal articles, conferences, and white papers were reviewed for IoHT authentication data technologies in relation to the problem statement of remote authentication and user management systems. Only publications written in English from the last decade were included (2012-2022) to identify key issues within the current health care practices and their management of IoHT devices. We discuss the components of the IoHT architecture from the perspective of data management and sensitivity to ensure privacy for all users. The data model addresses the security requirements of IoHT users, environments, and devices toward the automation of AMFA in health care. We found that in health care authentication, the significant threats occurring were related to data breaches owing to weak security options and poor user configuration of IoHT devices. The security requirements of IoHT data architecture and identified impactful methods of cybersecurity for health care devices, data, and their respective attacks are discussed. Data taxonomy provides better understanding, solutions, and improvements of user authentication in remote working environments for security features.

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“…Thara D.K., et al, in their paper, "Impact of Big Data in Healthcare: A Survey" analyses several research efforts made in the healthcare domain using Big Data concepts and methodologies. Along with this, the paper also provides an insight for the upcoming researchers to know the impact of Big Data on healthcare and get information about few research efforts made in healthcare using Big Data so far [8].…”

Section: Introductionmentioning

confidence: 96%

Technological and Scientific Developments Towards Use of Big Data in Health Data Management – An Overview

Bengeri¹

2022

IJSREM

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The healthcare industry has been using data and technology driven innovation for a long time now with Evidence Based Medicine (EBM) being at the helm. Effectively using technology is vital towards successful data management in healthcare. A convenient approach that can collect, store, evaluate, and analyze health data will be beneficial to all the stakeholders in the healthcare delivery chain. With the advancement, clubbed with the availability and affordability of technology, it has become an effective tool for providing the right- care backed with accurate, consolidated evidence. Such approach not only helps in improving the quality of care, it also helps in early detection of diseases and effective treatment, Big Data is providing the necessary data management tools for the same. A simple Extract-Transform-Load (ETL) procedure using Hadoop can be used. As Big data analyses large amounts of data to uncover hidden patterns, correlations and other insights, its role in making the right, predictive decisions for quality care of the patients and better health planning is possible. Big data technologies such as Hadoop bring significant cost advantages when it comes to storing large amounts of data and combined with the speed of Hadoop and in-memory analytics, with the ability to analyze new sources of data, we are able to analyze information instantly – and make decisions which is very essential in the healthcare provider segment. Hadoop does not only store, but also process any and many file data: big or small, can be plain text files or binary files like images and even multiple different version of some particular data format across different time periods. It consists of three core components: a distributed file system, a parallel programming framework, and a resource/job management system. In healthcare, patient records, health plans, insurance information and other types of information is extremely difficult to manage – but, this very data is full of key insights, the need is for an effective data management and analytics that can be pragmatic. By analyzing large amounts of information – both structured and unstructured – rapidly, health care providers can provide lifesaving diagnoses. Keywords: Big Data, EBM, ETL, Hadoop

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Impact of big data in healthcare: A survey

Cited by 17 publications

References 7 publications

Big Data Classification and Internet of Things in Healthcare

Big Data Classification and Internet of Things in Healthcare

A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things

Technological and Scientific Developments Towards Use of Big Data in Health Data Management – An Overview

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