Relational Forecast Limiter Algorithm for ICD based EMRs

  • Authors

    • Nithya. M
    • Sheela T
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.18782
  • Accuracy, Generalization, Noise, Privacy, Suppression
  • Forecasting individual privacy based on acquired knowledge across associated or related diseases is often a concern during data publishing. EMR (Electronic Medical Record) of an individual have more than one associated diseases which are potential knowledge nodes for analyst to exploit privacy. RFL (Relational Forecast Limiter) algorithm aims in reducing the forecast or prediction level by introducing generalization, suppression and noise addition techniques based on relational forecast detection and relational forecast height of diseases classified in ICD (International Classification of Diseases) table. These techniques delimit the forecasting capability to bring privacy under control. Generalization and suppression techniques are applied on sensitive attributes while noise addition is applied on quasi identifiers. Generalization is realized at lower twig and branch level, while suppression and noise addition are realized at bough level. Primary objective of the algorithm focuses on sharing minimum privacy data enabling the data analyst to extract maximum useful information. Accuracy is retained to ensure data analysis yields useful information for social causes. Experimental results on privacy and accuracy loss demonstrates algorithm efficiency.

     

     

  • References

    1. [1] Acar Tamersoy et al. (2012). Anonymization of Longitudinal Electronic Medical Records. IEEE Trans Inf Technol Biomed. May; 16(3): 413–423.

      [2] Adebayo Omotosho and Justice Emuoyibofarhe (2014). A Criticism of the Current Security, Privacy and Accountability Issues in Electronic Health Records. International Journal of Applied Information Systems. Volume 7– No.8, September.

      [3] Amit Thakkar et al. (2015). Correlation Based Anonymization Using Generalization and Suppression for Disclosure Problems. Advances in Intelligent Informatics pp 581-592.

      [4] Carlos Moque et al. (2012). AnonymousData.co: A proposal for interactive anonymization of Electronic Medical Records. SciVerse ScienceDirect, Procedia Technology 743-752.

      [5] Khaled El Emam et al. (2009). Globally Optimal k-Anonymity Method for the De-Identification of Health Data. J Am Med Inform Assoc. Sep-Oct; 16(5): 670–682.

      [6] Khaled El Emam (2011). Methods for the de-identification of electronic health records for genomic research. Genome Med; 3(4): 25.

      [7] Mathai N et al. (2017). Electronic Health Record Management: Expectations, Issues, and Challenges. Journal of Health & Medical Informatics. DOI: 10.4172/2157-7420.1000265.

      [8] Melanie L. Balestra (2017). Electronic Health Records: Patient Care and Ethical and Legal Implications for Nurse Practitioners. The journal of Nurse Practitioners. Volume 13, Issue 2, Pages 105–111.

      [9] Raymond Heatherly et al. (2016). A multi-institution evaluation of clinical profile anonymization. Journal of the American Medical Informatics Association, Volume 23, Issue e1, 1 April, Pages e131–e137.

      [10] Soohyung Kim et al. (2017). Privacy-preserving data cube for electronic medical records: An experimental evaluation. International Journal of Medical Informatics, Volume 97, January, Pages 33-42.

      [11] Wayne Newhauser et al. (2014). Anonymization of DICOM Electronic Medical Records for Radiation Therapy. Comput Biol Med. Oct 1; 0: 134–140.

      [12] K. Vijayakumar, C. Arun, Analysis and selection of risk assessment frameworks for cloud based enterprise applicationsâ€, Biomedical Research, ISSN: 0976-1683 (Electronic), January 2017.

      [13] K. Vijayakumar, C.Arun, Automated risk identification using NLP in cloud based development environments Ambient Intel

  • Downloads

  • How to Cite

    M, N., & T, S. (2018). Relational Forecast Limiter Algorithm for ICD based EMRs. International Journal of Engineering & Technology, 7(3.34), 103-106. https://doi.org/10.14419/ijet.v7i3.34.18782