Model Fusion to Enhance the Clinical Acceptability of Long-Term Glucose Predictions

This paper presents the Derivatives Combination Predictor (DCP), a novel model fusion algorithm for making long-term glucose predictions for diabetic people. First, using the history of glucose predictions made by several models, the future glucose variation at a given horizon is predicted. Then, by accumulating the past predicted variations starting from a known glucose value, the fused glucose prediction is computed. A new loss function is introduced to make the DCP model learn to react faster to changes in glucose variations. The algorithm has been tested on 10 \textit{in-silico} type-1 diabetic children from the T1DMS software. Three initial predictors have been used: a Gaussian process regressor, a feed-forward neural network and an extreme learning machine model. The DCP and two other fusion algorithms have been evaluated at a prediction horizon of 120 minutes with the root-mean-squared error of the prediction, the root-mean-squared error of the predicted variation, and the continuous glucose-error grid analysis. By making a successful trade-off between prediction accuracy and predicted-variation accuracy, the DCP, alongside with its specifically designed loss function, improves the clinical acceptability of the predictions, and therefore the safety of the model for diabetic people

Study of Short-Term Personalized Glucose Predictive Models on Type-1 Diabetic Children

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions

Connection between dynamically derived initial mass function normalization and stellar population parameters

Date of Acceptance: 10/08/2014We report on empirical trends between the dynamically determined stellar initial mass function (IMF) and stellar population properties for a complete, volume-limited sample of 260 early-type galaxies from the ATLAS3D project. We study trends between our dynamically derived IMF normalization αdyn ≡ (M/L)stars/(M/L)Salp and absorption line strengths, and interpret these via single stellar population-equivalent ages, abundance ratios (measured as [α/Fe]), and total metallicity, [Z/H]. We find that old and alpha-enhanced galaxies tend to have on average heavier (Salpeter-like) mass normalization of the IMF, but stellar population does not appear to be a good predictor of the IMF, with a large range of αdyn at a given population parameter. As a result, we find weak αdyn-[α/Fe] and αdyn -Age correlations and no significant αdyn -[Z/H] correlation. The observed trends appear significantly weaker than those reported in studies that measure the IMF normalization via the low-mass star demographics inferred through stellar spectral analysis.Peer reviewe

The ATLAS3D project - XXIV. The intrinsic shape distribution of early-type galaxies

We use the ATLAS3D sample to perform a study of the intrinsic shapes of early-type galaxies, taking advantage of the available combined photometric and kinematic data. Based on our ellipticity measurements from the Sloan Digital Sky Survey Data Release 7, and additional imaging from the Isaac Newton Telescope, we first invert the shape distribution of fast and slow rotators under the assumption of axisymmetry. Theso-obtained intrinsic shape distribution for the fast rotators can be described with a Gaussian with a mean flattening of q=0.25 and standard deviation σq = 0.14, and an additional tail towards rounder shapes.The slow rotators are much rounder, and are well described with a Gaussian with mean q = 0.63 and σq =0.09. We then checked that our results were consistent when applying a different and independent method to obtain intrinsic shape distributions, by fitting the observed ellipticity distributions directly using Gaussian parametrizations for the intrinsic axis ratios. Although both fast and slow rotators are identified as early-type galaxies in morphological studies, and in many previous shape studies are therefore grouped together, their shape distributions are significantly different, hinting at different formation scenarios. The intrinsic shape distribution of the fast rotators shows similarities with the spiral galaxy population. Including the observed kinematic misalignment in our intrinsic shape study shows that the fast rotators are predominantly axisymmetric, with only very little room for triaxiality. For the slow rotators though there are very strong indications that they are (mildly) triaxial.PostprintPeer reviewe