<p> </p> <p><u>Objective</u>: To use combined glycemic (HbA1c) and body mass index Z-score (BMIZ) trajectories spanning the COVID-19 pandemic to identify high-risk subgroups of adolescents with diabetes mellitus.</p> <p><u>Research Design and Methods</u>: Retrospective cohort of adolescents 10-19 years with type 1 and type 2 diabetes with ≥1 visits at a large pediatric hospital January 2018—March 2020 (pre-pandemic) and April 2020—August 2021 (pandemic). Group based trajectory models were used to identify latent classes of combined BMIZ and HbA1c trajectories. Multinomial logistic regression was used to evaluate predictors of class membership, including Area Deprivation Index (ADI) (socioeconomic status proxy). </p> <p><u>Results</u>: The cohort included 1,322 youth with T1D (93% white, 7% Black) and 59 with T2D (53% Black, 47% white). For T1D, six trajectory classes emerged. Black youth were more likely to be in the class with worsening glycemic control and concurrent BMIZ decrease at pandemic onset (relative risk ratio [RRR] vs white: 3.0, 95% CI 1.3-6.8) or in the class with progressively worsening glycemic control and obesity (RRR 3.0, 95% CI 1.3-6.8), while those from the most deprived neighborhoods (RRR ADI tertile 3 vs 1: 1.9, 95% CI 1.2-2.9) were more likely to be in the class with stable obesity and glycemic control. For T2D, three distinct trajectories emerged, two of which experienced worsening glycemic control with concurrent BMIZ decline at pandemic onset. </p> <p><u>Conclusions</u>: Race and neighborhood deprivation were independently associated with distinct glycemic and BMIZ trajectory classes in youth with diabetes, highlighting persistent and widening disparities associated with the COVID-19 pandemic.</p>
Network pruning reduces the size of neural networks by removing (pruning) neurons such that the performance drop is minimal. Traditional pruning approaches focus on designing metrics to quantify the usefulness of a neuron which is often quite tedious and sub-optimal. More recent approaches have instead focused on training auxiliary networks to automatically learn how useful each neuron is however, they often do not take computational limitations into account. In this work, we propose a general methodology for pruning neural networks. Our proposed methodology can prune neural networks to respect pre-defined computational budgets on arbitrary, possibly non-differentiable, functions. Furthermore, we only assume the ability to be able to evaluate these functions for different inputs, and hence they do not need to be fully specified beforehand. We achieve this by proposing a novel pruning strategy via constrained reinforcement learning algorithms. We prove the effectiveness of our approach via comparison with state-of-the-art methods on standard image classification datasets. Specifically, we reduce 83 − 92.90% of total parameters on various variants of VGG while achieving comparable or better performance than that of original networks. We also achieved 75.09% reduction in parameters on ResNet18 without incurring any loss in accuracy.
<p> </p> <p><u>Objective</u>: To use combined glycemic (HbA1c) and body mass index Z-score (BMIZ) trajectories spanning the COVID-19 pandemic to identify high-risk subgroups of adolescents with diabetes mellitus.</p> <p><u>Research Design and Methods</u>: Retrospective cohort of adolescents 10-19 years with type 1 and type 2 diabetes with ≥1 visits at a large pediatric hospital January 2018—March 2020 (pre-pandemic) and April 2020—August 2021 (pandemic). Group based trajectory models were used to identify latent classes of combined BMIZ and HbA1c trajectories. Multinomial logistic regression was used to evaluate predictors of class membership, including Area Deprivation Index (ADI) (socioeconomic status proxy). </p> <p><u>Results</u>: The cohort included 1,322 youth with T1D (93% white, 7% Black) and 59 with T2D (53% Black, 47% white). For T1D, six trajectory classes emerged. Black youth were more likely to be in the class with worsening glycemic control and concurrent BMIZ decrease at pandemic onset (relative risk ratio [RRR] vs white: 3.0, 95% CI 1.3-6.8) or in the class with progressively worsening glycemic control and obesity (RRR 3.0, 95% CI 1.3-6.8), while those from the most deprived neighborhoods (RRR ADI tertile 3 vs 1: 1.9, 95% CI 1.2-2.9) were more likely to be in the class with stable obesity and glycemic control. For T2D, three distinct trajectories emerged, two of which experienced worsening glycemic control with concurrent BMIZ decline at pandemic onset. </p> <p><u>Conclusions</u>: Race and neighborhood deprivation were independently associated with distinct glycemic and BMIZ trajectory classes in youth with diabetes, highlighting persistent and widening disparities associated with the COVID-19 pandemic.</p>
Recently, there has been a lot of interest in using neural networks for solving partial differential equations. A number of neural network-based partial differential equation solvers have been formulated which provide performances equivalent, and in some cases even superior, to classical solvers. However, these neural solvers, in general, need to be retrained each time the initial conditions or the domain of the partial differential equation changes. In this work, we posit the problem of approximating the solution of a fixed partial differential equation for any arbitrary initial conditions as learning a conditional probability distribution. We demonstrate the utility of our method on Burger's Equation.
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