Skip to main content
SpringerLink
Log in

Mining Standardized Semantic Interoperable Electronic Healthcare Records

  • Conference paper
Biomedical Informatics and Technology (ACBIT 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 404))

Included in the following conference series:

Abstract

There is vast potential for data mining applications in healthcare. Generally, these applications can be grouped as the evaluation of treatment effectiveness; management of healthcare; customer relationship management; and detection of fraud and abuse. To build the knowledge that is universally true, data has to be collected from all over the world. Collecting such large amount of data to prepare a single database that can be used to apply data mining techniques requires many challenges to be faced by the researcher. Till date no standard is adopted universally that imposes some guidelines on storing the data in a particular format. We propose to use a generic database to collect standardized EHR data that is available in different formats and at different geographical regions. This paper proposes a framework for applying data mining techniques to healthcare database stored on the basis of row model. We also try to incorporate protective measures in the architecture for privacy of the user, try to secure the data collected from various sources and also use of authentication mechanism at interface level.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Koh, H.C., Tan, G.: Data Mining Applications in Healthcare. Journal of Healthcare Information Management 19(2) (2005)

    Google Scholar 

  2. Cios, K.J., Moore, G.W.: Uniqueness of Medical Data Mining. Artificial Intelligence in Medicine Journal 26(1-2), 1–24 (2002)

    Article  Google Scholar 

  3. Sachdeva, S., Bhalla, S.: Semantic Interoperability in Standardized Electronic Health Record Databases. ACM Journal of Data and Information Quality 3(1), Article 1 (April 2012)

    Google Scholar 

  4. Beale, T., Heard, S.: The openEHR architecture: Architecture overview. In: The openEHR release 1.0.2. openEHR Foundation (2008)

    Google Scholar 

  5. Duftschmid, G., Wrba, T., Rinner, C.: Extraction of standardized archetyped data from Electronic Health Record Systems based on the Entity-Attribute-Value Model. International Journal of Medical Informatics 79(8), 585–597 (2010)

    Article  Google Scholar 

  6. Opereffa, http://opereffa.chime.ucl.ac.uk/introduction.jsf

  7. Kawamoto, K., Del Fiol, G., Lobach, D.F., Jenders, R.A.: Standards for scalable clinical decision support: need, current and emerging standards, gaps, and proposal for progress. The Open Medical Journal 4, 235 (2010)

    Google Scholar 

  8. Beale, T., Heard, S.: Archetype definitions and principles. In: The openEHR release 1.0.2. openEHR Foundation (2005)

    Google Scholar 

  9. OpenEHR Community, http://www.openehr.org/ (accessed 5/09)

  10. CEN TC/251. European standardization of health informatics. ENV 13606 Electronic Health Record Communication, http://www.centc251.org/

  11. ISO 13606-1, Health informatics: Electronic health record communication. Part 1: RM, 1st edn. (2008)

    Google Scholar 

  12. ISO 13606-2. Health informatics: Electronic health record communication. Part 2: Archetype interchange specification, 1st edn. (2008)

    Google Scholar 

  13. HL7. Health level 7, http://www.hl7.org (accessed 11/10)

  14. Dinu, V., Nadkarni, P., Brandt, C.: Pivoting approaches for bulk extraction of Entity–Attribute–Value data. Computer Methods and Programs in Biomedicine 82, 38–43 (2006)

    Article  Google Scholar 

  15. El-Sappagh, H.S., El-Masri, S., Riad, A.M., Elmogy, M.: Electronic Health Record Data Model Optimized for Knowledge Discovery. International Journal of Computer Science Issues 9(5(1) (September 2012)

    Google Scholar 

  16. Chen, M.S., Han, J., Yu, P.S.: Data Mining: An Overview from a Database Perspective. IEEE Transaction on Knowledge and Data Engineering 8(6) (December 1996)

    Google Scholar 

  17. Dinu, V., Nadkarni, P.: Guidelines for the effective use of entity-attribute-value modeling for biomedical databases. International Journal of Medical Informatics 76, 769–779 (2007)

    Google Scholar 

  18. International Health Terminology Standards Development Organisation. Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), http://www.ihtsdo.org/snomed-ct/

  19. Regenstrief Institute. Logical Observation Identifiers Names and Codes (LOINC), http://loinc.org/

  20. National Library of Medicine. RxNorm, http://www.nlm.nih.gov/research/umls/rxnorm/

  21. World Health Organization. International Classification of Diseases (ICD), http://www.who.int/classifications/icd/en/

  22. Weka 3 - Data Mining with Open Source Machine Learning Software in Java, http://www.cs.waikato.ac.nz/ml/weka/

  23. Garner, S.R.: WEKA: The Waikato environment for knowledge analysis. In: Proceedings of the New Zealand Computer Science Research Students Conference, The University of Waikato, Hamilton, New Zealand, pp. 57–64 (1995)

    Google Scholar 

  24. NHIN: Interoperability for the National Health Information Network. IEEE, E-books (2005)

    Google Scholar 

  25. Kennelly, R.J.: IEEE 1073, Standard for medical device communications. In: Proceedings of the IEEE Systems Readiness Technology Conference (Autotestcon 1998), pp. 335–336. IEEE, Los Alamitos (1998)

    Google Scholar 

  26. Atalag, K., Kingsford, D., Paton, C., Warren, J.: Putting health record interoperability standards to work. J. Health Inf. 5, 1 (2010)

    Google Scholar 

  27. Agarwal, R., Johnson, C.: Securing electronic health records without impeding the flow of information. Intl. Journal of Medical Informatics (2007)

    Google Scholar 

  28. Clifton, C., Marks, D.: Security and Privacy implications of Data Mining. In: ACM SIGMOD Workshop on Data Mining and Knowledge Discovery (1996)

    Google Scholar 

  29. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy and Security Rules (1996), http://www.hhs.gov/ocr/privacy/

  30. Sweeney, L.: k-Anonymity: A model for protecting privacy. International Journal on Uncertainity, Fuzziness and Knowledge Based Systems (2002)

    Google Scholar 

  31. Biskup, J.: Usability Confinement of Server Reactions: Maintaining Inference-Proof Client Views by Controlled Interaction Execution. In: DNIS, Japan (2010)

    Google Scholar 

  32. Machanavajjhala, A., Gehrke, J., Kifer, D.: L-diversity: Privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data Engineering, Atlanta, GA, USA, April 3-8 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee Institute of Information Technology University, Sector-128, 201301, Noida, India

    Shivani Batra, Shelly Sachdeva, Pulkit Mehndiratta & Hem Jyotsana Parashar

Authors
  1. Shivani Batra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shelly Sachdeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pulkit Mehndiratta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hem Jyotsana Parashar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology, The University of Aizu, 965-8580, Aizuwakamatsu, Fukushima, Japan

    Tuan D. Pham

  2. Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, 108-8639, Tokyo, Japan

    Kazuhisha Ichikawa

  3. Chaos Technology Research Lab, 5-26-5 Seta, 520-2134, Otsu-shi, Oyama-Higa, Japan

    Mayumi Oyama-Higa

  4. Department of Biostatistics and Medical Informatics, Faculty of Medicine and Pharmacy, The Free University of Brussels, 103, 1090, Laarbeeklaan, Brussels, Belgium

    Danny Coomans

  5. Department of Computer Science, University of Muenster, Einsteinstrasse 62, 48149, Muenster, Germany

    Xiaoyi Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Batra, S., Sachdeva, S., Mehndiratta, P., Parashar, H.J. (2014). Mining Standardized Semantic Interoperable Electronic Healthcare Records. In: Pham, T.D., Ichikawa, K., Oyama-Higa, M., Coomans, D., Jiang, X. (eds) Biomedical Informatics and Technology. ACBIT 2013. Communications in Computer and Information Science, vol 404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54121-6_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-54121-6_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54120-9

  • Online ISBN: 978-3-642-54121-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation