Additional Info

  • Status: Current
  • Coordinator: IRCCS Fondazione Salvatore Maugeri, Pavia (IT)
  • Other centers: Atene, Valencia
  • Area: Diabetic
  • Start date: Tuesday, 01 January 2013
  • Funding: EU
  • Solutions: i2b2
  • Description:

    MOSAIC is an EU-funded project aimed at providing an innovative approach for the diagnosis and the follow-up of the chronic diabetic population, to improve the characterization of patients and to help in evaluating the risk of developing type 2 DM (Diabetes mellitus) related complications.
    University of Pavia and BIOMERIS have implemented the i2b2 framework to collect and merge heterogeneous data coming from the IRCCS Fondazione S. Maugeri hospital EMR for diabetic patients, administrative data from the local health care agency and environmental data from regional databases.
    Once the system user selects the patient set of interest, the query engine retrieves from the i2b2 DW the necessary information, then the Data Mining module performs the Temporal Data Mining analysis and returns the control to the dashboard to display the processed data (figura 1). In practice, the MOSAIC architecture is already available for a routine use to obtain statistics on the diabetes center and to compute patient-tailored risk indexes. The MOSAIC data set includes geo-referenced clinical data that makes possible to geographically locate each subject.


    Figura 1: The IT architecture of the MOSAIC project



  • References:


    Dagliati A, Sacchi L, Bucalo M, Segagni D, Zarkogianni K, Martinez Millana A, Cancela J, Sambo F, Fico G, Meneu Barreira M, T, Cerra C, Nikita K, Cobelli C, Chiovato L, Arredondo M, T, Bellazzi R,  (2014) A data gathering framework to collect Type 2 diabetes patients data. In EEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 244-247. (link, pdf

    Bellazzi R, Dagliati A, Sacchi L, Segagni D. Big Data Technologies: New Opportunities for Diabetes Management. J Diabetes Sci Technol. 2015 Apr 24. pii: 1932296815583505. (link, pdf)

    Segagni D, Sacchi L, Dagliati A, Tibollo V, Leporati P, De Cata P, Chiovato L, Bellazzi R. Improving Clinical Decisions on T2DM Patients Integrating Clinical, Administrative and Environmental Data. Stud Health Technol Inform. 2015;216:682-6. (link, pdf)

    Martinez-Millana A, Fernandez-Llatas C, Sacchi L, Segagni D, Guillen S, Bellazzi R, Traver V.From data to the decision: A software architecture to integrate predictive modelling in clinical settings.Conf Proc IEEE Eng Med Biol Soc. 2015 Aug;2015:8161-4. (link, pdf)


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