ONCO-i2b2: Improve Patients Selection through Case-Based Information Retrieval Techniques

  • Daniele Segagni
  • Matteo Gabetta
  • Valentina Tibollo
  • Alberto Zambelli
  • Silvia G. Priori
  • Riccardo Bellazzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7348)

Abstract

The University of Pavia (Italy) and the IRCCS Fondazione Salvatore Maugeri hospital in Pavia have recently started an information technology initiative to support clinical research in oncology called ONCO-i2b2. This project aims at supporting translational research in oncology and exploits the software solutions implemented by the Informatics for Integrating Biology and the Bed-side (i2b2) research center. The ONCO-i2b2 software is designed to integrate the i2b2 infrastructure with the hospital information system, with the pathology unit and with a cancer biobank that manages both plasma and cancer tissue samples. Exploiting the medical concepts related to each patient, we have developed a novel data mining procedure that allows researchers to easily identify patients similar to those found with the i2b2 query tool, so as to increase the number of patients, compared to the patient set directly retrieved by the query. This allows physicians to obtain additional information that can support new insights in the study of tumors.

Keywords

i2b2 oncology case-based reasoning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Murphy, S.N., Weber, G., Mendis, M., Gainer, V., Chueh, H.C., Churchill, S., Kohane, I.: Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Stud. Health Technol. Inform. 169, 502–506 (2011)Google Scholar
  2. 2.
    Mate, S., Bürkle, T., Köpcke, F., Breil, B., Wullich, B., Dugas, M., Prokosch, H.U., Ganslandt, T.: Populating the i2b2 database with heterogeneous EMR data: a semantic network approach. Stud. Health Technol. Inform. 169, 502–506 (2011)Google Scholar
  3. 3.
    Sobin, L.H., Gospodarowicz, M.K.: Christian Wittekind TNMClassification of Malignant Tumours, 7th edn. Wiley (2009)Google Scholar
  4. 4.
    Xiang, Y., Lu, K., James, S.L., Borlawsky, T.B., Huang, K., Payne, P.R.: k-Neighborhood decentraliza-tion: A comprehensive solution to index the UMLS for large scale knowledge discovery. J. Biomed. Inform., December 2 (2011)Google Scholar
  5. 5.
    Melton, G.B., et al.: Inter-patient distance metrics using SNOMED CT defining relationships. J. Biomed. Inform. 39(6), 697–705 (2006)CrossRefGoogle Scholar
  6. 6.
    Caviedes, J., Cimino, J.: Towards the development of a conceptual distance metric for the UMLS. J. Biomed. Inform. 37, 77–85 (2004)CrossRefGoogle Scholar
  7. 7.
    InfoVis JavaScipt Toolkit, http://www.thejit.org/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniele Segagni
    • 1
  • Matteo Gabetta
    • 2
  • Valentina Tibollo
    • 1
  • Alberto Zambelli
    • 1
  • Silvia G. Priori
    • 1
  • Riccardo Bellazzi
    • 1
    • 2
  1. 1.IRCCS Fondazione Salvatore MaugeriItaly
  2. 2.Dipartimento di Ingegneria Industriale e dell’InformazioneUniversità degli Studi di PaviaItaly

Personalised recommendations