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 ledto the development of a computational model that recapitulates the aetiology of thedisease and simulates the immunological and metabolic alterations linked to type-2diabetes subjected to clinical, physiological, and behavioural features of prototypicalhuman individuals.Results: We analysed the time course of 46,170 virtual subjects, experiencing differentlifestyle 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 thedetailed mathematical model, ready to be implemented on mobile devices to allowself-assessment by informed and aware individuals. The computational model used togenerate the dataset of this work is available as a web-service at the following address:http://kraken.iac.rm.cnr.it/T2DM

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices / Stolfi, P; Valentini, I; Palumbo, M C; Tieri, P; Grignolio Corsini, A; Castiglione, F. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - (2021). [10.1186/s12859-020-03763-4]

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

Grignolio Corsini A;
2021-01-01

Abstract

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 ledto the development of a computational model that recapitulates the aetiology of thedisease and simulates the immunological and metabolic alterations linked to type-2diabetes subjected to clinical, physiological, and behavioural features of prototypicalhuman individuals.Results: We analysed the time course of 46,170 virtual subjects, experiencing differentlifestyle 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 thedetailed mathematical model, ready to be implemented on mobile devices to allowself-assessment by informed and aware individuals. The computational model used togenerate the dataset of this work is available as a web-service at the following address:http://kraken.iac.rm.cnr.it/T2DM
2021
Machine learning
Computational modeling
Synthetic data
Random forest
Emulator
T2D
File in questo prodotto:
File Dimensione Formato  
Stolfi, P., Valentini, I., Palumbo, M. C., Tieri, P., Grignolio, A., Castiglione, F._Potential predictors of type-2 diabetes risk. Machine learning and wearable health devices_BMC Bioinformatics_2020.pdf

solo gestori archivio

Tipologia: Post-print (versione valutata in peer review)
Licenza: Altra licenza
Dimensione 2.19 MB
Formato Adobe PDF
2.19 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
s12859-020-03763-4.pdf

accesso aperto

Tipologia: PDF editoriale (versione pubblicata dall'editore)
Licenza: Creative commons
Dimensione 2.06 MB
Formato Adobe PDF
2.06 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/150316
Citazioni
  • ???jsp.display-item.citation.pmc??? 7
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 4
social impact