Based on data measured during an oral glucose tolerance test, machine learning techniques were implemented to derive a simple empirical index for the estimation of the pancreatic beta-cell function in pregnant women, as assessed by mathematical modelling (beta-cell glucose sensitivity parameters). We studied a group of 84 pregnant women, who were analyzed by measuring and assessing a wide set of variables and parameters. Through a LASSO regularized support vector machine, we analyzed such wide batteries of variables/parameters and identified an index based on a simple algebraic equation (including glucose and C-peptide measurements only), which can predict the beta-cell glucose sensitivity with good accuracy (R2=0.64, p<0.0001, in the test set). In conclusion, the index is a good surrogate marker for the assessment of model-based beta-cell glucose sensitivity in pregnant women, thus it can be useful for easy application in the clinical context, where modelling analysis is not always possible.
Empirical Index for Easy Assessment of Pancreatic Beta-Cell Glucose Sensitivity During Pregnancy: A Machine Learning Approach / Salvatori, B.; Linder, T.; Eppel, D.; Morettini, M.; Gobl, C.; Tura, A.. - (2022), pp. 01-04. (Intervento presentato al convegno 10th E-Health and Bioengineering Conference, EHB 2022 tenutosi a Grigore T. Popa University of Medicine and Pharmacy, 16 Universitatii Street, rou nel 2022) [10.1109/EHB55594.2022.9991268].
Empirical Index for Easy Assessment of Pancreatic Beta-Cell Glucose Sensitivity During Pregnancy: A Machine Learning Approach
Morettini M.;
2022-01-01
Abstract
Based on data measured during an oral glucose tolerance test, machine learning techniques were implemented to derive a simple empirical index for the estimation of the pancreatic beta-cell function in pregnant women, as assessed by mathematical modelling (beta-cell glucose sensitivity parameters). We studied a group of 84 pregnant women, who were analyzed by measuring and assessing a wide set of variables and parameters. Through a LASSO regularized support vector machine, we analyzed such wide batteries of variables/parameters and identified an index based on a simple algebraic equation (including glucose and C-peptide measurements only), which can predict the beta-cell glucose sensitivity with good accuracy (R2=0.64, p<0.0001, in the test set). In conclusion, the index is a good surrogate marker for the assessment of model-based beta-cell glucose sensitivity in pregnant women, thus it can be useful for easy application in the clinical context, where modelling analysis is not always possible.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
