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Journal articles on the topic 'Big data in healthcare'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Viceconti, Marco, Peter Hunter, and Rod Hose. "Big Data, Big Knowledge: Big Data for Personalized Healthcare." IEEE Journal of Biomedical and Health Informatics 19, no. 4 (July 2015): 1209–15. http://dx.doi.org/10.1109/jbhi.2015.2406883.

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2

Sarkar, Bikash Kanti. "Big Data and Healthcare Data." International Journal of Knowledge-Based Organizations 7, no. 4 (October 2017): 50–77. http://dx.doi.org/10.4018/ijkbo.2017100104.

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Big data and its analytics yield a lot of opportunities to make great progresses in many fields, ranging from economic and business activities to public administration, from national security to scientific researches and so on. However, the most noticeable point is that healthcare data has been recently identified as a prime example of big data. Undoubtedly, efficient use of healthcare resources has become a key factor in improving overall healthcare system. But for managing healthcare data and obtaining potential results, we need integration and sharing of data that ultimately demand the concept of distributed system. The paper in its first phase gives an overview on big data and healthcare data from different aspects. A review on the state-of-the-art distributed file system (Hadoop) is conducted in this stage too. The primary aim of this phase is to provide an overall picture on big data as well as healthcare data for non-expert readers. In the next phase, a cloud-based e-health system is proposed for the expert audiences. The expected promising characteristics as well as the managerial implications of the model are highlighted in the analysis section.

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Sadiku, Matthew N. O. "Big Data in Healthcare." International Journal for Research in Applied Science and Engineering Technology 7, no. 9 (September 30, 2019): 1165–68. http://dx.doi.org/10.22214/ijraset.2019.9167.

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4

Shukla, Purvika. "Big Data: Healthcare Informatics." International Journal for Research in Applied Science and Engineering Technology V, no. XI (November 20, 2017): 1147–59. http://dx.doi.org/10.22214/ijraset.2017.11170.

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5

Pramanik, Pijush Kanti Dutta, Saurabh Pal, and Moutan Mukhopadhyay. "Big Data and Big Data Analytics for Improved Healthcare Service and Management." International Journal of Privacy and Health Information Management 8, no. 1 (January 2020): 13–51. http://dx.doi.org/10.4018/ijphim.2020010102.

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Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

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Kunnavil, Radhika. "Healthcare Data Utilization for the Betterment of Mankind - An Overview of Big Data Concept in Healthcare." International Journal of Healthcare Education & Medical Informatics 05, no. 02 (August 24, 2018): 14–17. http://dx.doi.org/10.24321/2455.9199.201807.

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7

Kim, Ha-Na. "Use of Healthcare Big Data." Korean Journal of Family Practice 7, no. 3 (June 20, 2017): 307. http://dx.doi.org/10.21215/kjfp.2017.7.3.307.

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8

Ryu, Seewon, and Tae-Min Song. "Big Data Analysis in Healthcare." Healthcare Informatics Research 20, no. 4 (2014): 247. http://dx.doi.org/10.4258/hir.2014.20.4.247.

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9

Asri, Hiba, Hajar Mousannif, and Hassan Al Moatassime. "Big Data Analytics in Healthcare." International Journal of Distributed Systems and Technologies 10, no. 4 (October 2019): 45–58. http://dx.doi.org/10.4018/ijdst.2019100104.

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Sensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transmitted to pregnant woman to make quick decisions; with the intervention of her doctor; through an android mobile application that we created. As well, she receives recommendations based on her behavior. We used real-world data to validate the system and assess its performance and effectiveness. Experiments were made using the Big Data Platform Databricks.

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Belle, Ashwin, Raghuram Thiagarajan, S. M. Reza Soroushmehr, Fatemeh Navidi, Daniel A. Beard, and Kayvan Najarian. "Big Data Analytics in Healthcare." BioMed Research International 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/370194.

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The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.

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11

Undavia, Jaimin Navinchandra, and Atul Manubhai Patel. "Big Data Analytics in Healthcare." International Journal of Big Data and Analytics in Healthcare 5, no. 1 (January 2020): 19–27. http://dx.doi.org/10.4018/ijbdah.2020010102.

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The technological advancement has also opened up various ways to collect data through automatic mechanisms. One such mechanism collects a huge amount of data without any further maintenance or human interventions. The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. High level of sophistication has been incorporated in almost all the industry, and healthcare is one of them. The article shows that the existence of huge amount of data in healthcare industry and the data generated in healthcare industry is neither homogeneous nor a simple type of data. Then the various sources and objectives of data are also highlighted and discussed. As data come from various sources, they must be versatile in nature in all aspects. So, rightly and meaningfully, big data analytics has penetrated the healthcare industry and its impact is also highlighted.

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12

Lv, Zhihan, and Liang Qiao. "Analysis of healthcare big data." Future Generation Computer Systems 109 (August 2020): 103–10. http://dx.doi.org/10.1016/j.future.2020.03.039.

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13

Venkateswara Reddy, R., and Dr D. Murali. "Analyzing Indian healthcare data with big data." International Journal of Engineering & Technology 7, no. 3.29 (August 24, 2018): 88. http://dx.doi.org/10.14419/ijet.v7i3.29.18467.

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Big Data is the enormous amounts of data, being generated at present times. Organizations are using this Big Data to analyze and predict the future to make profits and gain competitive edge in the market. Big Data analytics has been adopted into almost every field, retail, banking, governance and healthcare. Big Data can be used for analyzing healthcare data for better planning and better decision making which lead to improved healthcare standards. In this paper, Indian health data from 1950 to 2015 are analyzed using various queries. This healthcare generates the considerable amount of heterogeneous data. But without the right methods for data analysis, these data have become useless. The Big Data analysis with Hadoop plays an active role in performing significant real-time analyzes of the enormous amount of data and able to predict emergency situations before this happens.

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Yamamoto, Ryuichi. "Healthcare Big Data and Personal Data Protection:." Iryo To Shakai 26, no. 1 (2016): 85–93. http://dx.doi.org/10.4091/iken.26.85.

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15

Ann Alexander, Cheryl, and Lidong Wang. "Big Data and Data-Driven Healthcare Systems." Journal of Business and Management Sciences 6, no. 3 (June 25, 2018): 104–11. http://dx.doi.org/10.12691/jbms-6-3-7.

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16

Bakker, LJ, J. Aarts, and WK Redekop. "Is Big Data in Healthcare about Big Hope or Big Hype? Early Health Technology Assessment of Big Data Analytics in Healthcare." Value in Health 19, no. 7 (November 2016): A705. http://dx.doi.org/10.1016/j.jval.2016.09.2058.

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17

Wang, Lidong, and Cheryl Ann Alexander. "Big Data Analytics in Healthcare Systems." International Journal of Mathematical, Engineering and Management Sciences 4, no. 1 (February 1, 2019): 17–26. http://dx.doi.org/10.33889/ijmems.2019.4.1-002.

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Big Data analytics can improve patient outcomes, advance and personalize care, improve provider relationships with patients, and reduce medical spending. This paper introduces healthcare data, big data in healthcare systems, and applications and advantages of Big Data analytics in healthcare. We also present the technological progress of big data in healthcare, such as cloud computing and stream processing. Challenges of Big Data analytics in healthcare systems are also discussed.

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18

Juneja, Sapna, Abhinav Juneja, and Rohit Anand. "Healthcare 4.0-Digitizing Healthcare Using Big Data for Performance Improvisation." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4408–10. http://dx.doi.org/10.1166/jctn.2020.9087.

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The management, analysis and usage of the data in the technical industry have been changed by the introduction of the Big Data Technology. There are numerous areas where usage of big data is very promising but the main industry that is getting maximum benefit from big data is healthcare industry. Introduction of big data into healthcare affected a lot to healthcare industry. Most of the advantages are reduction in the cost of the treatment, decision making in case of any emergency, prevention of curable diseases and early prediction of epidemics etc. This usage of big data in healthcare is also helpful in improvising the average lifetime of people. Like any other industry, Health professionals are also using a huge amount of data and using that data in performing various treatments and procedures to get benefited from it. In this paper, we will discuss about the usage of big data in healthcare, the various sources of data, benefits of that data and the obstacles in using that data and tools, technologies and algorithms used to get, analyze and process that data.

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19

Muradova, Gulara. "IMPLEMENTATION OF BIG DATA IN HEALTHCARE." Problems of Information Technology 07, no. 2 (July 19, 2016): 83–90. http://dx.doi.org/10.25045/jpit.v07.i2.10.

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20

Tiwari, RajeewPrabhat, Girdhar Verma, Sukanya Ghildiyal, and SyedShariq Naeem. "Technology, Healthcare, and Big Data Analytics." MAMC Journal of Medical Sciences 5, no. 3 (2019): 103. http://dx.doi.org/10.4103/mamcjms.mamcjms_67_19.

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21

Gao, G., S. Koch, and S. S. L. Tan. "Big Data and Analytics in Healthcare." Methods of Information in Medicine 54, no. 06 (2015): 546–47. http://dx.doi.org/10.3414/me15-06-1001.

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SummaryThis editorial is part of the Focus Theme of Methods of Information in Medicine on “Big Data and Analytics in Healthcare”.The amount of data being generated in the healthcare industry is growing at a rapid rate. This has generated immense interest in leveraging the availability of healthcare data (and “big data”) to improve health outcomes and reduce costs. However, the nature of healthcare data, and especially big data, presents unique challenges in processing and analyzing big data in healthcare. This Focus Theme aims to disseminate some novel approaches to address these challenges. More specifically, approaches ranging from efficient methods of processing large clinical data to predictive models that could generate better predictions from healthcare data are presented.

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22

Chellappan, Sriram, Nirmalya Roy, and Sajal K. Das. "Special Issue–Big Data for Healthcare." Pervasive and Mobile Computing 28 (June 2016): 1–2. http://dx.doi.org/10.1016/j.pmcj.2016.04.002.

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23

Alexander, Cheryl Ann, and Lidong Wang. "Healthcare Driven by Big Data Analytics." American Journal of Engineering and Applied Sciences 11, no. 3 (March 1, 2018): 1154–63. http://dx.doi.org/10.3844/ajeassp.2018.1154.1163.

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24

Spintge, Ralph, and Joanne V. Loewy. "Big Data and Priorities in Healthcare." Music and Medicine 10, no. 2 (April 30, 2018): 52. http://dx.doi.org/10.47513/mmd.v10i2.623.

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Big Data is changing health care and health professions rapidly and fundamentally. One national company promises to extract from their data statements like: we know which patient will die within 6 weeks or a year, we know how much her/his treatment will cost, and so forth

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25

Bahri, Safa, Nesrine Zoghlami, Mourad Abed, and Joao Manuel R. S. Tavares. "BIG DATA for Healthcare: A Survey." IEEE Access 7 (2019): 7397–408. http://dx.doi.org/10.1109/access.2018.2889180.

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26

Kasten, Joseph E. "Big Data Applications in Healthcare Administration." International Journal of Big Data and Analytics in Healthcare 5, no. 2 (July 2020): 12–37. http://dx.doi.org/10.4018/ijbdah.2020070102.

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The healthcare industry has a growing record of using big data-related technologies such as data analytics, internet of things, and machine learning, especially in the clinical areas. However, healthcare institutions must also perform all of the administrative processes just as any other organization. Thus, like many other industries, healthcare has begun to apply these same technologies to improve their understanding of these internal operations and use them to make better decisions and run a more effective operation. This study takes a structured literature review approach to describe the current state of this literature and identify the major themes and priorities of both the research community and the healthcare industry as a whole. The contribution made by this study is to provide a comprehensive analysis of the state of the literature to use as a foundation for the future research opportunities noted in the paper.

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27

Kim, Hun-Sung, and Dai-Jin Kim. "Dementia Research Using Healthcare Big Data." Dementia and Neurocognitive Disorders 18, no. 3 (2019): 73. http://dx.doi.org/10.12779/dnd.2019.18.3.73.

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28

Aziz, Hassan A. "Handling Big Data in Modern Healthcare." Laboratory Medicine 47, no. 4 (October 4, 2016): e38-e41. http://dx.doi.org/10.1093/labmed/lmw038.

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Shaikh, Tawseef Ayoub, and Rashid Ali. "Big data for better Indian healthcare." International Journal of Information Technology 11, no. 4 (August 3, 2019): 735–41. http://dx.doi.org/10.1007/s41870-019-00342-6.

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30

YOON, Hyung-Jin. "Medical big data for smart healthcare." Annals of Hepato-Biliary-Pancreatic Surgery 25, no. 1 (June 30, 2021): S27. http://dx.doi.org/10.14701/ahbps.bp-sy-3-2.

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31

THANGARASU, GUNASEKAR, and KAYALVIZHI SUBRAMANIAN. "BIG DATA ANALYTICS IN HEALTHCARE SERVICES." Science Proceedings Series 1, no. 2 (April 24, 2019): 22–24. http://dx.doi.org/10.31580/sps.v1i2.561.

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This study addresses the healthcare services problems which focus on the upcoming and promising areas of medical research and proposed a novel approach integrating in big data analytics and Apache. The proposed approach will improve the healthcare services fastly and efficiently. The big data analytics can continually evaluate clinical data in order to improve the effective practices of physicians and improved patient care

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Raja, Rakesh, Indrajit Mukherjee, and Bikash Kanti Sarkar. "A Systematic Review of Healthcare Big Data." Scientific Programming 2020 (July 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/5471849.

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Over the past decade, data recorded (due to digitization) in healthcare sectors have continued to increase, intriguing the thought about big data in healthcare. There already exists plenty of information, ready for analysis. Researchers are always putting their best effort to find valuable insight from the healthcare big data for quality medical services. This article provides a systematic review study on healthcare big data based on the systematic literature review (SLR) protocol. In particular, the present study highlights some valuable research aspects on healthcare big data, evaluating 34 journal articles (between 2015 and 2019) according to the defined inclusion-exclusion criteria. More specifically, the present study focuses to determine the extent of healthcare big data analytics together with its applications and challenges in healthcare adoption. Besides, the article discusses big data produced by these healthcare systems, big data characteristics, and various issues in dealing with big data, as well as how big data analytics contributes to achieve a meaningful insight on these data set. In short, the article summarizes the existing literature based on healthcare big data, and it also helps the researchers with a foundation for future study in healthcare contexts.

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Aroul Canessane, R., J. Albert Mayan, R. DhanaLakshmi, Ragini Singh, and Sushmita Bhowmik. "Privacy Prevention in Healthcare Data Using Big Data." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3576–81. http://dx.doi.org/10.1166/jctn.2019.8327.

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The use of the patient’s information in biomedical research or healthcare research is increasing rapidly. We are using big data to generate and collect a large amount of personal information of patients. The security of patients individual data have turned into an extraordinary threat as it might prompt spillage of delicate data which can put the patient’s protection in danger. There are various measures which have been taken to protect the data from attack. The relevant paper reviews relevant topics in the context of healthcare research. We will discuss the consequences of big data privacy in healthcare research and a better way to improve the data privacy in healthcare research or biomedical research.

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Shahid, Arsalan, Thien-An Ngoc Nguyen, and M.-Tahar Kechadi. "Big Data Warehouse for Healthcare-Sensitive Data Applications." Sensors 21, no. 7 (March 28, 2021): 2353. http://dx.doi.org/10.3390/s21072353.

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Obesity is a major public health problem worldwide, and the prevalence of childhood obesity is of particular concern. Effective interventions for preventing and treating childhood obesity aim to change behaviour and exposure at the individual, community, and societal levels. However, monitoring and evaluating such changes is very challenging. The EU Horizon 2020 project “Big Data against Childhood Obesity (BigO)” aims at gathering large-scale data from a large number of children using different sensor technologies to create comprehensive obesity prevalence models for data-driven predictions about specific policies on a community. It further provides real-time monitoring of the population responses, supported by meaningful real-time data analysis and visualisations. Since BigO involves monitoring and storing of personal data related to the behaviours of a potentially vulnerable population, the data representation, security, and access control are crucial. In this paper, we briefly present the BigO system architecture and focus on the necessary components of the system that deals with data access control, storage, anonymisation, and the corresponding interfaces with the rest of the system. We propose a three-layered data warehouse architecture: The back-end layer consists of a database management system for data collection, de-identification, and anonymisation of the original datasets. The role-based permissions and secured views are implemented in the access control layer. Lastly, the controller layer regulates the data access protocols for any data access and data analysis. We further present the data representation methods and the storage models considering the privacy and security mechanisms. The data privacy and security plans are devised based on the types of collected personal, the types of users, data storage, data transmission, and data analysis. We discuss in detail the challenges of privacy protection in this large distributed data-driven application and implement novel privacy-aware data analysis protocols to ensure that the proposed models guarantee the privacy and security of datasets. Finally, we present the BigO system architecture and its implementation that integrates privacy-aware protocols.

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35

Hajirahimova, Makrufa. "THE BIG DATA ERA IN HEALTHCARE: PROMISES AND CHALLENGES." Problems of Information Technology 08, no. 1 (January 24, 2017): 64–72. http://dx.doi.org/10.25045/jpit.v08.i1.08.

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36

Bayrak, Ebru Aydindag, and Pinar Kirci. "A Brief Survey on Big Data in Healthcare." International Journal of Big Data and Analytics in Healthcare 5, no. 1 (January 2020): 1–18. http://dx.doi.org/10.4018/ijbdah.2020010101.

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This article presents a brief introduction to big data and big data analytics and also their roles in the healthcare system. A definite range of scientific researches about big data analytics in the healthcare system have been reviewed. The definition of big data, the components of big data, medical big data sources, used big data technologies in present, and big data analytics in healthcare have been examined under the different titles. Also, the historical development process of big data analytics has been mentioned. As a known big data analytics technology, Apache Hadoop technology and its core components with tools have been explained briefly. Moreover, a glance of some of the big data analytics tools or platforms apart from Hadoop eco-system were given. The main goal is to help researchers or specialists with giving an opinion about the rising importance of used big data analytics in healthcare systems.

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kaur, Jagjit, and Purnima Popli. "Smart Healthcare Approach using Big Data: Big Opportunities and Challenges." CGC International Journal of Contemporary Technology and Research 2, no. 2 (June 26, 2020): 101–5. http://dx.doi.org/10.46860/cgcijctr.2020.06.26.101.

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Big data’ is defined as the collection of large amount of data in digital form irrespective of the fact that it is whether structured or not. In this modern era, vast amount of data is created everywhere everyday at exponential rates. The primary objective of this paper is to provide deep analysis in the area of healthcare industry using big data. There is vast possibility of advanced patient care and decision support for physical and clinical data. As hospitals stores records of various patients and by collecting and analyzing this clinical data available from various geographical locations, we can predicts the behavior analysis and symptoms of patients under critical conditions and later by analyzing this data we can improve our medication facilities and service centers. In this article, we have discussed the usage of big data in healthcare and corresponding decision support and how the processing steps can be carried out for providing benefits to society.

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Stylianou, Andreas, and Michael A. Talias. "Big data in healthcare: a discussion on the big challenges." Health and Technology 7, no. 1 (December 14, 2016): 97–107. http://dx.doi.org/10.1007/s12553-016-0152-4.

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Leung, Kelvin TakYiu, and Anne L. Stevenson. "Application of Big Data in Decision Making for Emergency Healthcare Management." International Journal of Research and Engineering 5, no. 2 (March 2018): 311–14. http://dx.doi.org/10.21276/ijre.2018.5.2.2.

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Soljak, Michael. "Big Voice or Big Data? The Difficult Birth of care.data." Journal of Medical Law and Ethics 3, no. 1 (August 25, 2015): 135–42. http://dx.doi.org/10.7590/221354015x14319325750232.

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In the United Kingdom (UK), a range of historical national healthcare data collections have occurred, in some cases without a very specific legal basis apart from overarching international and European Union data protection commitments expressed in the UK Data Protection Act 1998. In 2012, the English Government announced that the GP Extraction Service (now care. data), a new central flow of patient-identifiable healthcare data from general practice computer systems, would commence to support healthcare commissioning. Data on the whole population would be extracted, and specific patient consent would not be sought. UK primary healthcare data is characterised by its richness, and comprises demographic, diagnostic, clinical, prescribing, test results and a range of other classes of data. In 2014 the English media and several non-governmental patient organisations began a campaign questioning the care. data initiative, and uncovered quite limited but nonetheless damaging evidence of improper release of patient data from historical data sources. A subsequent national review of information governance and a parliamentary inquiry has delayed care. data implementation, and a patient opt-out is being introduced. Another positive effect is the subsequently much higher public awareness of care.data.

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41

López, Victoria, Diego Urgelés, Óscar Sánchez, and Gabriel Valverde. "Big Data in Healthcare and Social Sciences." International Journal of Information Systems and Social Change 8, no. 3 (July 2017): 1–16. http://dx.doi.org/10.4018/ijissc.2017070101.

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Healthcare providers and payers are increasingly turning to Big Data and analytics, to help them understand their patients and the context of their illnesses in more detail. Industry leaders are exploring/using Big Data to reduce costs, increase efficiency and improve patient care. The next future is an innovative approach to improving patient access using lean methods and predictive analytics. Social sciences are very much related to healthcare and both areas develop in a parallel way. In this article, we introduce one example of application: Bip4cast (a bipolar disorder CAD system). This paper shows how Bip4cast deals with different data sources to enrich the knowledge and improve predictive analysis.

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42

Shukur, Mohammed Hussein, Laith R. Fliah, and Aram Mohammed. "Challenges Smartphone’s Big Data in HealthCare Systems." Cihan University-Erbil Scientific Journal 2017, Special-1 (2017): 120–25. http://dx.doi.org/10.24086/cuesj.si.2017.n1a10.

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43

Karl, Yoki, Haeng Kon Kim, and Jong-Hak Lee. "Big Data Management System for U-Healthcare." International Journal of Software Innovation 9, no. 1 (January 2021): 1–11. http://dx.doi.org/10.4018/ijsi.2021010101.

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U-Healthcare monitoring module can sometimes be monitored by using downloadable tracking applications necessary for its services due to the popularity of the smart phone devices, thus continuing to provide new services and healthcare contents and information at a lower cost but the need to analyze larger collections of data continues to evolve as time progresses. As u-healthcare service is published as an alternative to addressing national issues such as aging population, solitary elderly people, or even the child care monitoring, the related monitoring care is expected to grow beyond the normal data that it caters resulting to tougher data management. In this paper, the authors proposed a system that could help handle certain issues in u-healthcare big data management.

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Dinov, Ivo D. "Volume and value of big healthcare data." Journal of Medical Statistics and Informatics 4, no. 1 (2016): 3. http://dx.doi.org/10.7243/2053-7662-4-3.

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45

Joshi, Sayali Avinash. "Big Data and Its Applications in Healthcare." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 4049–52. http://dx.doi.org/10.22214/ijraset.2018.4668.

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46

Kong, Hyoun-Joong. "Managing Unstructured Big Data in Healthcare System." Healthcare Informatics Research 25, no. 1 (2019): 1. http://dx.doi.org/10.4258/hir.2019.25.1.1.

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47

Gupta, Shubham, and Robin Rao. "A REVIEW: BIG DATA IN HEALTHCARE APPLICATIONS." International Journal of Advanced Research 6, no. 9 (August 31, 2018): 307–11. http://dx.doi.org/10.21474/ijar01/7676.

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48

Khaloufi, Hayat, Karim Abouelmehdi, Abderrahim Beni-hssane, and Mostafa Saadi. "Security model for Big Healthcare Data Lifecycle." Procedia Computer Science 141 (2018): 294–301. http://dx.doi.org/10.1016/j.procs.2018.10.199.

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Mounia, Bouhriz, and Chaoui Habiba. "Big Data Privacy in Healthcare Moroccan Context." Procedia Computer Science 63 (2015): 575–80. http://dx.doi.org/10.1016/j.procs.2015.08.387.

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ALEXANDRU, Adriana Gabriela, Irina Miruna RADU, and Madalina Lavinia BIZON. "Big Data in Healthcare - Opportunities and Challenges." Informatica Economica 22, no. 2/2018 (June 30, 2018): 43–54. http://dx.doi.org/10.12948/issn14531305/22.2.2018.05.

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