The prime attention towards biomedical research is of a great significance when we take into the account the importance of
human health and various emergency, medical and clinical issues associated with it since it claims to have a high importance in
understanding and accelerating the medical research and associated subjects and also the revolution by applying machine learning to
massive health care information. Yet biomedical research is not only drowning in data, but also starving for knowledge. Current
challenges in biomedical research include information overloading. The need to combine large amounts of structured, semi-structured,
and vast amounts of unstructured information where the big data concept comes into the use and the need to optimize processes,
progression of work and guidelines advancement, in order to increase capacity while it simultaneously reduces the costs providing
improving efficiencies . It is our hope that explaining these connections will decode these techniques and provide a set of reasonable
expectations for the role of machine learning and big data in health care. Therefore, the biomedical and healthcare communities gained a
huge growth in fields like big data and machine learning, which lead to medical data benefits like patient care, community services and
early disease detection which is explained in detail further.
Published In:IJCSN Journal Volume 8, Issue 2
Date of Publication : April 2019
Pages : 120-124
Figures :--
Tables : --
Prudhvi Veeravelley :
is Currently pursuing B.TECH
III-Year,CSE at GITAM UNIVERSITY,HYDERAAD,His Research
Interests areArtificia Intelleigence,Machine Learning.,Predictive
Analytics.
Varsha Sree Katragadda :
is Currently pursuing
B.TECH III-Year,CSE at GITAM UNIVERSITY,HYDERAAD,His
Research Interests area Machine Learning.
Rishik Chandra Tammineedi :
is Currently pursuing B.TECH
III-Year,CSE at GITAM UNIVERSITY,HYDERAAD,Her Research
Interests areArtificia Intelleigence,Machine Learning,Artificial
Intelleigence.
K Radha :
working as an Asst Professor at GITAM
University,Hyderabad. She has Completed M.Tech(CSE) at
JNTUH,Pursuing PhD at KL University,Vijayawada.She has 12 years of
Teaching Experience and 1Year Industrial Experience.She has published
numerous research papers and presented at Various conferences.She is a
Member of IAENG and Reviewer for IJECE.
There is an exigent want for consolidative and interactive
machine learning solutions, as a result of no medical
doctor or medicine research worker will keep up these
days with the more and more massive and complex.
Though machine learning and big data may seem
mysterious at first, they are in fact deeply related to
traditional bibliometrics that are recognizable. It is our
hope that explaining these connections will decode these
techniques and provide a set of reasonable expectations for
the role of machine learning and big data in health care.
Therefore, the biomedical and healthcare communities
gained a huge growth in fields like big data and
machine learning, which lead to medical data benefits
like patient care, community services and early disease
detection.
[1] Anastasia. "Brief history of Big Data"
CLEVERISM(magazine) , 2016.
[2] Nasrabadi, Nasser M. "Pattern recognition and machine
learning." Journal of electronic imaging 16.4 (2007):
049901.
[3 ]Krumholz, Harlan M. "Big data and new knowledge in
medicine: the thinking, training, and toolsneeded for a
learning health system." Health Affairs 33.7 (2014):
1163-1170.
[4] Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis,
G.K. and Taha, K., 2015. "Efficient machine learning for
big data:" A review. Big Data Research, 2(3), pp.87-93.
[5] Sager N, Siegel JS, Larmon EA. "Natural Language
Information Processing: a computer grammar of English
and its applications." Reading, MA: Addison-Wesley;
1981 Dec.
[6] Lukowicz, Paul. "Guest editorial: Wearable computing
and artificial intelligence for healthcare applications."
Artificial intelligence in medicine 42, no. 2 (2008): 95-
98.
[7] Jürgen Bajorath, John Overington, Jeremy L Jenkins , Pat
Walters."Drug discovery and development in the era of
Big Data". FUTURE MEDICINAL CHEMISTRYVOL.
8, NO. 15.
[8] Chen, Feng, et al. "Data mining for the internet of things:
literature review and challenges." International Journal
of Distributed Sensor Networks 11.8 (2015): 431047.
[9] Papamitsiou, Zacharoula, and Anastasios A.
Economides. "Learning analytics and educational data
mining in practice: A systematic literature review of
empirical evidence." Journal of Educational
Technology & Society 17, no. 4 (2014).
[10] Qiu, Junfei, Qihui Wu, Guoru Ding, Yuhua Xu, and
Shuo Feng. "A survey of machine learning for big data
processing." EURASIP Journal on Advances in Signal
Processing 2016, no. 1 (2016): 67.
[11] Papamitsiou, Zacharoula, and Anastasios A.
Economides. "Learning analytics and educational data
mining in practice: A systematic literature review of
empirical evidence." Journal of Educational
Technology & Society 17, no. 4 (2014).
[12] Papamitsiou Z, Economides AA. Learning analytics and
educational data mining in practice: A systematic
literature review of empirical evidence. Journal of
Educational Technology & Society. 2014 Oct 1;17(4).
[13] (2003) Breast cancer facts and figures 2003-2004.
American Cancer Society.
[14] Karabatak M, Cevdet M (2009) An expert system for
detection of breast cancer based on association rules and
neural network. Expert Systems with Applications 36:
3465-3469
[15] Kovalerchuc B, Triantaphyllou E, Ruiz JF, Clayton
J (1997) Fuzzy logic in computer-aided breastcancer
diagnosis: Analysis of lobulation. Artif Intell
Med11: 75-85.
[16] Pendharkar PC, Rodger JA, Yaverbaum GJ, Herman N,
Benner M (1999) Association, statistical, mathematical
and neural approaches for mining breast cancer
patterns. Expert Systems with Applications 17: 223-
232.
[17] Dempster AP, Laird NM, Rubin DB (1977)
Maximum Likelihood from Incomplete Data
via the EM Algorithm. J R Stat Soc Series B
39: 1-38.
[18] Rubin DB, Schenker N (1991) Multiple
Imputation in Health-Care Databases - an overview and some applications. Stat Med 10:
585-598.
[19] Weka 3: Data Mining Software in Java.
[20] Cristianini N, Shawe-taylor J (2000) An Introduction to
Support Vector Machines and Other Kernel-based
Learning Methods, London: Cambridge University
Press.
[21] Joachims T (1998) Making large-scale support
vector machine learning practical. Advances in
Kernel Methods: Support Vector Learning. MIT
Press, Cambridge, MA, 169-184.
[22] Haykin S (1998) Neural networks: a comprehensive
foundation. New Jersey: Prentice Hall, New Jersey,
USA.
[23] Hornik K, Stinchcombe M, White H (1990)
Universal approximation of an unknown
mapping and its derivatives using multilayer feed
forward networks. Neural Networks 3: 359-366.
[24] Grimmer, Justin. "We are all social scientists now:
how big data, machine learning, and causal inference
work together." PS: Political Science & Politics 48.1
(2015): 80-83.
Park, S., Bong, K., Shin, D., Lee, J., Choi, S., & Yoo,
H. J. (2015, February). 4.6 A1. 93TOPS/W scalable
deep learning/inference processor with tetra-parallel
MIMD architecture for big-data applications. In Solid-
State Circuits Conference-(ISSCC), 2015 IEEE
International (pp. 1-3). IEEE.
[25] Ghahramani, Zoubin. "Probabilistic machine learning
and artificial intelligence." Nature 521.7553 (2015):
452.
[26] Gu, D., Li, J., Li, X., & Liang, C. (2017). Visualizing
the knowledge structure and evolution of big data
research in healthcare informatics. International
journal of medical informatics, 98, 22-32.
[27] Roski, J., Bo-Linn, G.W. and Andrews, T.A., 2014.
Creating value in health care through big data:
opportunities and policy implications. Health affairs,
33(7), pp.1115-1122.
[28] Luo J, Wu M, Gopukumar D, Zhao Y. Big data
application in biomedical research and health care:
a literature review. Biomedical informatics
insights. 2016 Jan;8:BII-S31559.
