Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subjected to clinical, physiological, and behavioural features of prototypical human individuals.

Authors

  • Paola Stolfi
    Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy. p.stolfi@iac.cnr.it.
  • Ilaria Valentini
    Institute of Aerospace Medicine "A. Di Loreto", Rome, Italy.
  • Maria Concetta Palumbo
    Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy.
  • Paolo Tieri
    Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy.
  • Andrea Grignolio
    Research Ethics and Integrity Interdepartmental Center, National Research Council of Italy, Rome, Italy.
  • Filippo Castiglione
    Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy.