Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.
BMC Bioinformatics
; 21(Suppl 17): 508, 2020 Dec 14.
Article
en En
| MEDLINE
| ID: mdl-33308172
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 .Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Diabetes Mellitus Tipo 2
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Aprendizaje Automático
Tipo de estudio:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Italia