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Annals of Computer Science and Information Systems, Volume 14

Proceedings of the 2017 International Conference on Information Technology and Knowledge Management

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A Survey on Advanced Approaches of EHR in inter-related data using Machine Learning

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DOI: http://dx.doi.org/10.15439/2017KM10

Citation: Proceedings of the 2017 International Conference on Information Technology and Knowledge Management, Ajay Jaiswal, Vijender Kumar Solanki, Zhongyu (Joan) Lu, Nikhil Rajput (eds). ACSIS, Vol. 14, pages 113119 ()

Full text

Abstract. Medical data is being used for huge number of research works over the globe which is for predicting something novel case studies in each work. The current research which we are handling is on utilizing the EHR (Electronic health Records) data in an efficient way based on the cause -- effect ratio and the variables available for the data manipulation, processing and generating efficient data for designing efficient prediction models. In this research we are focusing on the congenital tethered cord syndrome through which some many functional outcomes issues are recording in different cases and there is a wide range of scope for research. In this research we are identifying the data from different EHR applications and designing the architecture to gather valuable data set from those for designing prediction model for predicting functional outcomes of health and life in patients with congenital deformity. Through EHR applications we gather information and BigData is being created in this sector. Data inter --relation is explained in this survey article in an efficient way with respect to medical domain. EHR data will be hosted over the cloud and in public repositories. Will focus on those categories in an efficient manner.

References

  1. “An improved approach for prediction of Parkinson’s Disease using Machine Learning Techniques”, Kamal Narayan Reddy Challa* ,2016, IEEE
  2. Adoption of Electronic Health Record System: Multiple Theoretical Perspective”, Qiwei Gan, Qing Cao – 2014 IEEE
  3. “A Scalable mHealth System for Noncommunicable Disease Management”, G D Clifford* - 2014 IEEE
  4. “Predictive Medication and use of BigData”, Avijit Goswami – 2017 IEEE
  5. “Variation in Outcome in Tethered Cord Syndrome”, Norulain Iqbal*, 2016, Asian Spine Journal
  6. “Resource Frequency Prediction in Healthcare: Machine Learning Approach” Daniel Vieira, 2016 IEEE
  7. National Patient Safety Association, “Safer care for acutely ill patients:Learning from serious accidents,” Tech. Rep., 2007.
  8. National Institute for Clinical Excellence, “Recognition of and response to acute illness in adults in hospital,” Tech. Rep., 2007.
  9. H. Gao, A. McDonnell, D. Harrison, S. Adam, K. Daly, L. Esmonde, D. Goldhill, G. Parry, A. Rashidian, C. Subbe, and S. Harvey, “Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward,” Intensive Care Med., vol. 33, no. 4, pp. 667–679, 2007.
  10. L. Tarassenko, D. Clifton, M. Pinsky, M. Hravnak, J. Woods, and P.Watkinson, “Centile-based early warning scores derived from statistical distributions of vital signs,” Resuscitation, vol. 82, no. 8, pp. 1013–1018, 2011.
  11. D. Prytherch, G. Smith, P. Schmidt, P. Featherstone, K. Stewart,D.Knight,and B. Higgins, “Calculating early warning scores—A classroom comparisonof pen and paper and hand-held computer methods,” Resuscitation,vol. 70, pp. 173–178, 2006.
  12. A. Hann, “Multi-parameter monitoring for early warning of patient deterioration,”Ph.D. dissertation, Univ. Oxford, Oxford, U.K., 2008.
  13. D. Wong, I. Strachan, and L. Tarassenko,“Visualisation of highdimensional data for very large data sets,” presented at the Workshop Mach. Learn. Healthcare Appl., Helsinki, Finland, 2008.
  14. B. Schölkopf, J. Platt, J. Shawe-Taylor,A. J. Smola, and R C. Williamson, “Estimating the support of a high-dimensional distribution,” Neural Comput., vol. 13, no. 7, pp. 1443–1471, 2001.
  15. S. Hugueny, D. Clifton, and L. Tarassenko, “Probabilistic patient monitoring with multivariate, multimodal extreme value theory,” Commun. Comput. Sci., vol. 127, pp. 199–211, 2011.
  16. R. Kavitha, E. Kannan, S. Kotteswaran,"Implementation of Cloud based Electronic Health Record (EHR) for Indian Healthcare Needs",Indian Journal of Science and Technology,2016 Jan, 9(3), http://dx.doi.org/10.17485/ijst/2016/v9i3/86391
  17. Meenakshi Sharma, Himanshu Aggarwal,“EHR Adoption in India: Potential and the Challenges”,Indian Journal of Science and Technology, 2016 Sep, 9(34), http://dx.doi.org/10.17485/ijst/2016/v9i34/100211
  18. ResScan Software : ResScan version 4.2 Clinical Guide from “ResMed Ltd 1 Elizabeth Macarthur Drive Bella Vista NSW 2153 Australia”
  19. R. Lozano, and C. J. L. Murray, “Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010,” The Lancet, vol. 380, no. 9859, pp. 2095–2128, 2012.
  20. B. A. Payne, J. A. Hutcheon, “Arisk prediction model for the assessment and triage of women with hypertensivedisorders of pregnancy in low-resourced settings: The miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) multi-country prospective cohort study,” PLoS Med., vol. 11, no. 1, e1001589, pp. 1–13, 2013.
  21. W. Karlen, G. A. Dumont, C. Petersen, J. Gow, J. Lim, J. Sleiman, and J. M. Ansermino, “Human-centered phone oximeter interface design for the operating room,” in Proc. Inter. Conf. Health form., Rome, Italy, 2011, pp. 433–438.
  22. University of British Columbia. (2014) Community level interventions for pre-eclampsia (CLIP). [Online]. Available: http://clinicaltrials.gov/ct2/show/NCT01911494. NLM Identifier: NCT01911494