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of Computer Engineering in Research Trends (IJCERT)

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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Big data in healthcare: Challenges and approaches

P.Murthuja, , , ,
Affiliations
Associate professor, Prabhath Institute of Computer Science, Parnapalli village , Bandi atmakur, Kurnool District, Andhra Pradesh, India
:10.22362/ijcert/2019/v6/i05/v6i0502


Abstract
Now a day’s huge volume of data is generated due to wide usage of social media, online shopping or transactions gives delivery to big data. Visual representation and analysis of this large volume of data is one of the major research topics today. Healthcare is one of the most promising areas for using big data for change. Big data healthcare has enormous potential to improve patient outcomes, obtain valuable information, prevent disease, reduce healthcare delivery costs and improve quality of life. In this paper i focus on challenges associated with healthcare big data and also explore the common approaches for analysing big data in health Care system.


Citation
P.Murthuja."Big data in healthcare: Challenges and approaches". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.6, Issue 05,pp.317-321, May - 2019, URL :https://ijcert.org/ems/ijcert_papers/V6I502.pdf,


Keywords : Healthcare, Big data analytics, Internet of things, personalized medicine.

References
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[2] Sunidhi Sharma, D.K.Sharma, Supriti Sharma, “Text Analysis and Sentiment Analysis using Facebook in R Language: Case studies” , International Journal of Computer and Mathematical Sciences, ISSN 2347-8527, vol 6 Issue 12, December 2017.

[3]	Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo, “Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark,  September 7-11, 2017.

[4]	Kishaloy Halder, Lahari Poddar, Min-Yen Kan, “Modeling Temporal Progression of Emotional Status in Mental Health Forum : a Recurrent Neural Net Approach” Proceedings of Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 127-135, Copenhagen, Denmark, September 7-11, 2017.

[5]	Jeremy Barnes, Roman Klinger, and Sabine Schulte im Walde, “Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets”  Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 2-12, Copenhagen, Denmark, September 7-11, 2017.

[6]	Keenen Cates, Pengcheng Xiao, *, Zeyhang, Calvin Dailey, “Can Emoticons Be Used To Predict Sentiment?”  Journal of Data Science 355-376, April 04, 2018.

[7]	Prabaharan Poornachandran,, “deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets”  Proceedings of the 8th Workshop on Computational Approaches to Subjectivity , Sentiment and Social media Analysis, pages 102-111, Copenhagen, Denmark, September 7-11, 2017. 
[8] Murphy G, Hanken MA, Waters K. Electronic health records: changing the vision. Philadelphia: Saunders W B Co;1999. p. 627. 
[9]  Shameer K, et al. Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams. Brief Bioinform. 2017;18(1):105–24. 
[10] Service, R.F. The race for the $1000 genome. Science. 2006;311(5767):1544–6.


DOI Link : https://doi.org/10.22362/ijcert/2019/v6/i05/v6i0502

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DOI:10.22362/ijcert