Paola Stolfi1, Ilaria Valentini2, Maria Concetta Palumbo3, Paolo Tieri3, Andrea Grignolio4,5, Filippo Castiglione3. 1. Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy. p.stolfi@iac.cnr.it. 2. Institute of Aerospace Medicine "A. Di Loreto", Rome, Italy. 3. Institute for Applied Mathematics, National Research Council of Italy, Rome, Italy. 4. Research Ethics and Integrity Interdepartmental Center, National Research Council of Italy, Rome, Italy. 5. Medical Humanities - International MD Program, Vita-Salute San Raffaele University, Milan, Italy.
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. RESULTS: We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. CONCLUSIONS: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .
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. RESULTS: We analysed the time course of 46,170 virtual subjects, experiencing different lifestyle conditions. We then set up a statistical model able to recapitulate the simulated outcomes. CONCLUSIONS: The resulting machine learning model adequately predicts the synthetic dataset and can, therefore, be used as a computationally-cheaper version of the detailed mathematical model, ready to be implemented on mobile devices to allow self-assessment by informed and aware individuals. The computational model used to generate the dataset of this work is available as a web-service at the following address: http://kraken.iac.rm.cnr.it/T2DM .
Authors: Bradley A Fritz; Yixin Chen; Teresa M Murray-Torres; Stephen Gregory; Arbi Ben Abdallah; Alex Kronzer; Sherry Lynn McKinnon; Thaddeus Budelier; Daniel L Helsten; Troy S Wildes; Anshuman Sharma; Michael Simon Avidan Journal: BMJ Open Date: 2018-04-10 Impact factor: 2.692