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The prediction of obstructive atherosclerotic disease has significant clinical meaning for the decision making. In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk. The machine learning methodology includes five steps: the preprocessing of the input data, the class imbalance handling applying the Easy Ensemble algorithm, the recursive feature elimination technique implementation, the implementation of gradient boosting classifier, and finally the model evaluation, while the fine tuning of the presented model was implemented through a randomized search optimization of the model's hyper-parameters over an internal 3-fold cross-validation. In total, 187 participants with suspicion of CAD previously underwent CTCA during EVINCI and ARTreat clinical studies and were prospectively included to undergo follow-up CTCA. The predictive model was trained using imaging data (geometrical and blood flow based) and non-imaging data. The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data. The innovative aspect of the proposed study is the combination of imaging-based data with the typical CAD risk factors to provide an integrated CAD risk-predictive model. [ABSTRACT FROM AUTHOR]

This study aims at presenting a nonlinear, recursive, multivariate prediction model of the subcutaneous glucose concentration in type 1 diabetes. Nonlinear regression is performed in a reproducing kernel Hilbert space, by either the fixed budget quantized kernel least mean square (QKLMS-FB) or the approximate linear dependency kernel recursive least-squares (KRLS-ALD) algorithm, such that a sparse model structure is accomplished. A multivariate feature set (i.e., subcutaneous glucose, food carbohydrates, insulin regime and physical activity) is used and its influence on short-term glucose prediction is investigated. The method is evaluated using data from 15 patients with type 1 diabetes in free-living conditions. In the case when all the input variables are considered: (i) the average root mean squared error (RMSE) of QKLMS-FB increases from 13.1 mg dL-1 (mean absolute percentage error (MAPE) 6.6%) for a 15-min prediction horizon (PH) to 37.7 mg dL-1 (MAPE 20.8%) for a 60-min PH and (ii) the RMSE of KRLS-ALD, being predictably lower, increases from 10.5 mg dL-1 (MAPE 5.2%) for a 15-min PH to 31.8 mg dL-1 (MAPE 18.0%) for a 60-min PH. Multivariate data improve systematically both the regularity and the time lag of the predictions, reducing the errors in critical glucose value regions for a PH ≥ 30 min. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

Glucose concentration in type 1 diabetes is a function of biological and environmental factors which present high inter-patient variability. The objective of this study is to evaluate a number of features, which are extracted from medical and lifestyle self-monitoring data, with respect to their ability to predict the short-term subcutaneous (s.c.) glucose concentration of an individual. Random forests (RF) and RReliefF algorithms are first employed to rank the candidate feature set. Then, a forward selection procedure follows to build a glucose predictive model, where features are sequentially added to it in decreasing order of importance. Predictions are performed using support vector regression or Gaussian processes. The proposed method is validated on a dataset of 15 type diabetics in real-life conditions. The s.c. glucose profile along with time of the day and plasma insulin concentration are systematically highly ranked, while the effect of food intake and physical activity varies considerably among patients. Moreover, the average prediction error converges in less than d/2 iterations (d is the number of features). Our results suggest that RF and RReliefF can find the most informative features and can be successfully used to customize the input of glucose models. [ABSTRACT FROM AUTHOR]

Background: The prevention of hypoglycemic events is of paramount importance in the daily management of insulin-treated diabetes. The use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly toward this direction. The literature suggests that, although the recent glucose profile is a prominent predictor of hypoglycemia, the overall patient's context greatly impacts its accurate estimation. The objective of this study is to evaluate the performance of a support vector for regression (SVR) s.c. glucose method on hypoglycemia prediction. Materials and Methods: We extend our SVR model to predict separately the nocturnal events during sleep and the nonnocturnal (i.e., diurnal) ones over 30-min and 60-min horizons using information on recent glucose profile, meals, insulin intake, and physical activities for a hypoglycemic threshold of 70 mg/dL. We also introduce herein additional variables accounting for recurrent nocturnal hypoglycemia due to antecedent hypoglycemia, exercise, and sleep. SVR predictions are compared with those from two other machine learning techniques. Results: The method is assessed on a dataset of 15 patients with type 1 diabetes under free-living conditions. Nocturnal hypoglycemic events are predicted with 94% sensitivity for both horizons and with time lags of 5.43 min and 4.57 min, respectively. As concerns the diurnal events, when physical activities are not considered, the sensitivity is 92% and 96% for a 30-min and 60-min horizon, respectively, with both time lags being less than 5 min. However, when such information is introduced, the diurnal sensitivity decreases by 8% and 3%, respectively. Both nocturnal and diurnal predictions show a high ( > 90%) precision. Conclusions: Results suggest that hypoglycemia prediction using SVR can be accurate and performs better in most diurnal and nocturnal cases compared with other techniques. It is advised that the problem of hypoglycemia prediction should be handled differently for nocturnal and diurnal periods as regards input variables and interpretation of results. [ABSTRACT FROM AUTHOR]

Background/Objectives: Obesity and overweight have become increasingly prevalent in different populations of people with type 1 diabetes (PwT1D). This study aimed to assess the effect of body weight on glycaemic indices in PwT1D. Methods: Adult PwT1D using continuous glucose monitoring (CGM) and followed up at a regional academic diabetes centre were included. Body weight, body mass index (BMI), waist circumference, glycated haemoglobin (HbA1c), and standard CGM glycaemic indices were recorded. Glycaemic indices were compared according to BMI, and correlation and linear regression analysis were performed to estimate the association between measures of adiposity and glycaemic indices. Results: A total of 73 PwT1D were included (48% normal weight, 33% overweight, and 19% obese). HbA1c was 7.2% (5.6–10), glucose management indicator (GMI) 6.9% (5.7–8.9), coefficient of variation (CV) for glucose 39.5% ± 6.4, mean glucose 148 (101–235) mg/dL, TIR (time in range, glucose 70–180 mg/dL) 66% (25–94), TBR70 (time below range, 54–69 mg/dL) 4% (0–16), TBR54 (<54 mg/dL) 1% (0–11), TAR180 (time above range, 181–250 mg/dL) 20% ± 7, and TAR250 (>250 mg/dL) 6% (0–40). Glycaemic indices and achievement (%) of optimal glycaemic targets were similar between normal weight, overweight, and obese patients. BMI was associated negatively with GMI, mean glucose, TAR180, and TAR250 and positively with TIR; waist circumference was negatively associated with TAR250. Conclusions: CGM-derived glycaemic indices were similar in overweight/obese and normal weight PwT1D. Body weight and BMI were positively associated with better glycaemic control. PwT1D should receive appropriate ongoing support to achieve optimal glycaemic targets whilst maintaining a healthy body weight. [ABSTRACT FROM AUTHOR]