Predictive Healthcare Informatics using Deep Learning - A Big Data Approach
Keywords:
Big Data, Predictive System, Healthcare, Deep Learning, ClinicalAbstract
Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an exceptional speed and scale. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new deep learning approaches, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Predictive analysis applications in health care can determine the patients who are at the risk of developing certain conditions such as diabetes, asthma and other lifetime illnesses. The clinical decision support systems incorporate predictive analytics to support medical decision making in the domains like health-care. This paper aims to build a predictive system on health care domain using deep learning approaches on Big data.
References
- Jake Luo, Min Wu et.al. Big Data Application in Biomedical Research and Health Care: A Literature Review Biomedical Informatics Insights 2016:8
- Gianluca Bontempi, Yann-A¨el Le Borgne, Predictive modeling in a big data distributed setting: a scalable bias correction Approach, 978-1-5090-2622-7/16 $31.00 © 2016 IEEE Big DataCongress.2016.17
- Najafabadi et al., Deep learning applications and challenges in big data analytics Journal of Big Data (2015) 2:1, DOI 10.1186/s40537-014-0007-7
- J.Archenaa and E.A.Mary Anita, A Survey Of Big Data Analytics in Healthcare and Government, 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15) doi: 10.1016/j.procs.2015.04.021
- Shortliffe EH, Cimino JJ. Biomedical Informatics. Berlin: Springer; 2014.
- Gerda Claeskens and Nils Lid Hjort. Model selection and model averaging. Cambridge series in statistical and probabilistic mathematics. Cambridge University Press, Cambridge, New York, 2008.
- Bengio Y, LeCun Y (2007) Scaling learning algorithms towards, AI. In: Bottou L, Chapelle O, DeCoste D, Weston J (eds). Large Scale Kernel Machines. MIT Press, Cambridge, MA Vol. 34. pp 321–360. http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+lecun_chapter2007.pdf
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001.
- http://digitalindia.gov.in/content/recent-e-governance-initiatives.
- J. G. Van Manen et al., “The influence of COPD on health-related quality of life independent of the influence of comorbidity,” J. Clin. Epidemiol., vol. 56, no. 12, pp. 1177–1184, 2003.
- "Mirror, Mirror on the Wall: An International Update on the Comparative Performance of American Health Care". The Commonwealth Fund. May 15, 2007. Retrieved March 7, 2009.
- Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304--310.
- Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
- University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
- University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
- V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:Robert Detrano, M.D., Ph.D.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.