Disease Predictive, Best Drug : Big Data Implementation of Drug Query with Disease Prediction, Side Effects & Feedback Analysis

Authors

  • Naveenraj I  Department of CSE, Panimalar Institute Of Technology, Chennai, Tamil Nadu, India
  • Thurab Ahmed A  Department of CSE, Panimalar Institute Of Technology, Chennai, Tamil Nadu, India
  • Dilly Ganesh N  Department of CSE, Panimalar Institute Of Technology, Chennai, Tamil Nadu, India
  • Gokula Krishnan  Department of CSE, Panimalar Institute Of Technology, Chennai, Tamil Nadu, India
  • Hema latha  Department of CSE, Panimalar Institute Of Technology, Chennai, Tamil Nadu, India
  • Subedha V  Department of CSE, Panimalar Institute Of Technology, Chennai, Tamil Nadu, India

Keywords:

Predictive and Personalized Query System

Abstract

In the existing system, several precautions should be taken in using pharmaceutical drugs, for both healthcare experts, who prescribe and administer drugs, and for drug purchasers. In the proposed gadget, side effects and effectiveness, relies upon on traits of patients, which include age, gender, existence, and genetic profiles. Our intention is to offer a device to help experts and consumers in locating and selecting pills. To reap this intention, we broaden an approach that lets in a person to query for pills that satisfy a fixed of situations based on drug residences, inclusive of drug symptoms, facet outcomes, and drug interactions, and additionally takes into account affected person profiles. The amendment paintings is the combination of big information and android primarily based input user with the aid of any consumer for smooth facts evaluation method. We also analyze the disease and satisfactory drug cautioned to that precise affected person via large facts analysis. Use can put up the query thru gadget or via android application additionally. We also set up appointment to the fine physician for the consultation based on person feedbacks.

References

  1. S. Khemmarat and L. Gao, "Supporting drug prescription via predictive and personalized query system," in PervasiveHealth. IEEE, 2015.
  2. C. Knox et al., "Drugbank 3.0: a comprehensive resource for omics research on drugs," Nucleic acids research, vol. 39, no. suppl 1, pp. D1035–D1041, 2011.
  3. M. Kuhn et al., "A side effect resource to capture phenotypic effects of drugs," Molecular systems biology, vol. 6, no. 1, p. 343, 2010.
  4. M. Kanehisa and S. Goto, "Kegg: kyoto encyclopedia of genes and genomes," Nucleic acids research, vol. 28, no. 1, pp. 27–30, 2000.
  5. T. Fawcett, "An introduction to roc analysis," Pattern recognition letters, vol. 27, no. 8, pp. 861–874, 2006.
  6. K. Sangkuhl et al., "Pharmgkb: understanding the effects of individual genetic variants," Drug Metab. Rev., vol. 40, no. 4, pp. 539–551, 2008.
  7. A. Langer et al., "A text based drug query system for mobile phones," Int. J. Mob. Commun., vol. 12, no. 4, pp. 411–429, Jul. 2014.
  8. C. Doulaverakis et al., "Panacea, a semantic-enabled drug recommendations discovery framework," J. Biomed. Semant., vol. 5, p. 13, 2014.
  9. M. Dumontier and N. Villanueva-Rosales, "Towards pharmacogenomics knowledge discovery with the semantic web," Briefings in bioinformatics, vol. 10, no. 2, pp. 153–163, 2009.
  10. A. Ben Abacha and P. Zweigenbaum, "Medical question answering: translating medical questions into sparql queries," in Proceedings of the 2nd ACM SIGHIT. ACM, 2012, pp. 41–50.
  11. J. Jin et al., "Querying web-scale information networks through bounding matching scores," in WWW 2015, 2015, pp. 527–537.
  12. J. Jin et al., "A distributed approach for top-k star queries on massive information networks," in ICPADS 2014, 2014, pp. 9–16.
  13. A. Khan et al., "Neighborhood based fast graph search in large networks," in SIGMOD 2011. ACM, 2011, pp. 901–912.
  14. A. Khan et al., "Nema: Fast graph search with label similarity," in Proc. of the VLDB Endowment, vol. 6, no. 3, 2013, pp. 181–192.
  15. J. Sun, H. Xu, and Z. Zhao, "Network-assisted investigation of antipsychotic drugs and their targets," Chemistry & biodiversity, vol. 9, no. 5, pp. 900–910, 2012.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-04-30

Issue

Volume 2, Issue 2, March-April-2017

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Naveenraj I, Thurab Ahmed A, Dilly Ganesh N, Gokula Krishnan, Hema latha, Subedha V, " Disease Predictive, Best Drug : Big Data Implementation of Drug Query with Disease Prediction, Side Effects & Feedback Analysis" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.195-202, March-April-2017.