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Distributed Contextualization of Biomedical Data: a case study in precision medicine

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  • Additional Information
    • Contributors:
      Software Stack for Massively Geo-Distributed Infrastructures (LS2N - équipe STACK); Inria Rennes – Bretagne Atlantique; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire des Sciences du Numérique de Nantes (LS2N); Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST); Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); Laboratoire des Sciences du Numérique de Nantes (LS2N); Département Automatique, Productique et Informatique (IMT Atlantique - DAPI); IMT Atlantique (IMT Atlantique); Centre de Recherche en Transplantation et Immunologie (U1064 Inserm - CRTI); Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE); Université de Nantes (UN)-Université de Nantes (UN); Université de Nantes (UN); Centre Hospitalier Universitaire de Nantes = Nantes University Hospital (CHU Nantes)
    • Publication Information:
      HAL CCSD
      IEEE
    • Publication Date:
      2020
    • Collection:
      Inserm: HAL (Institut national de la santé et de la recherche médicale)
    • Subject Terms:
    • Abstract:
      International audience ; An important aspect of precision medicine consists in patient-centered contextualization analyses that are used as part of biomedical interactive tools. Such analyses often harness data of large populations of patients from different research centers and can often benefit from a distributed implementation. However, performance and the security and privacy concerns of sharing sensitive biomedical data can become a major issue. We have investigated these issues in the context of a kidney transplanted patient contextualization project: the Kidney Transplantation Application (KITAPP). In this paper, we present a motivation for distributed implementations in this context, notably for computing percentiles for contextualization. We present a corresponding system architecture, motivate privacy and performance issues, and present a novel distributed implementation that is evaluated in a realistic multi-site setting.
    • Relation:
      hal-02922930; https://inria.hal.science/hal-02922930; https://inria.hal.science/hal-02922930/document; https://inria.hal.science/hal-02922930/file/Distributed_Contextualization_of_Biomedical_Data__a_case_study.pdf
    • Accession Number:
      10.1109/AICCSA50499.2020.9316502
    • Online Access:
      https://inria.hal.science/hal-02922930
      https://inria.hal.science/hal-02922930/document
      https://inria.hal.science/hal-02922930/file/Distributed_Contextualization_of_Biomedical_Data__a_case_study.pdf
      https://doi.org/10.1109/AICCSA50499.2020.9316502
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.D90D1E76