Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and ev…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
47 results for “glucose”
Machine learning detects subtle glucose changes for early diabetes diagnosis.
problem Challenging early-stage diabetes diagnosis due to subtle glucose changes.
method Applied machine learning to synthetic glucose profiles generated by a biophysical model.
result High accuracy (above 85%) in detecting insulin resistance using various neural networks.
Neural system optimizes glucose levels in diabetics.
problem Limited research on continuous glucose maintenance devices.
method Differential predictive control with neural policy and differentiable modeling.
result Improves glucose level optimization in real-time.
New framework predicts 5-year glucose values with missing data.
problem Significant missing data in longitudinal glucose studies.
method Reproducing Kernel Hilbert Spaces (RKHS) with missing responses analysis.
result Identifies new factors affecting long-term glucose evolution.
HAD-Net forecasts glucose levels with insights into insulin and carbs diffusion.
problem Inaccurate predictions in glucose level forecasting without context understanding.
method Hybrid model combining deep learning and physiological models, using recurrent attention network.
result Achieves competitive performance in glucose level forecasting with plausible diffusion insights.
New model predicts blood glucose in diabetics with improved accuracy.
problem Forecasting blood glucose in type 1 diabetics with high accuracy.
method Integrates machine learning with existing biomedical model to capture time-varying dynamics.
result Improved long-term forecasting of blood glucose up to 6 hours.
Deep RL improves blood glucose control for T1D patients.
problem Managing blood glucose levels for people with type 1 diabetes.
method Developed deep reinforcement learning techniques for automated blood glucose control.
result Deep RL approach outperforms baseline control algorithms, reducing glycemic risk and hypoglycemia.
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamicsstat.ML
New method estimates effects of multiple nutrients on blood glucose.
problem Estimating physiological response to multiple nutrient treatments.
method Convolution-based multi-output Gaussian process model.
result Improved prediction accuracy and better interpretation of individual nutrient effects.
A method models continuous-time glucose distributions in children with diabetes.
problem Capturing subtle temporal changes in glucose distributions.
method Probabilistic framework using Gaussian mixtures and neural ODEs.
result Detects treatment-related improvements in glucose dynamics.
Study finds RNNs predict STBG better than ARIMA, useful for diabetes patients.
problem Improving short-term blood glucose prediction for diabetes management.
method Investigated Recurrent Neural Networks (RNNs) and compared them to ARIMA for STBG prediction.
result Population-based RNN model outperforms ARIMA across various prediction horizons.
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to pred…
Modeling glucose distribution changes over time using neural ODEs.
problem Analyzing how continuous glucose distribution changes over time in diabetic patients.
method Combines Gaussian mixture, MMD, and Neural ODE to model temporal evolution of glucose distribution.
result Highly interpretable model detects subtle distribution shifts and remains computationally efficient.
With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven…
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering information between numeric physiological measurements and various drug prescriptions provided in E…
New MMD estimators detect differences in missing paired data.
problem Handling missing data in matched pairs with complex distributions.
method Maximum mean discrepancy (MMD) estimators for complex data with missing values.
result Valid and consistent estimators detect differences in data distributions.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, …
In many forecasting applications, it is valuable to predict not only the value of a signal at a certain time point in the future, but also the values leading up to that point. This is especially true in clinical applications, where the future state of the patient can be less important than the patient's overall traject…
RETAIN model improves glucose forecasting for diabetics, offering both accuracy and interpretability.
problem Inability of deep learning models to interpret their predictions in healthcare.
method Two-level attention mechanism in a recurrent neural network (RETAIN) architecture.
result RETAIN model achieves comparable accuracy to LSTM and FCN models while being highly interpretable.
CTRNNs improve blood glucose forecasting in ICU, outperforming traditional models.
problem Forecasting blood glucose in ICU with irregular measurements.
method Continuous time autoregressive recurrent neural networks (CTRNNs) using neural ODE or neural flow layers.
result CTRNNs generally outperform traditional autoregressive models in probabilistic forecasting of blood glucose.
Hybrid model learns interpretable meal-level glycemic control.
problem Lack of flexible, interpretable meal-level glycemic control methods.
method Hybrid variational autoencoder grounding latent space to mechanistic differential equation.
result Unsupervised representation discovers separation between individuals based on disease severity.
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related t…
Patients with Type I Diabetes (T1D) must take insulin injections to prevent the serious long term effects of hyperglycemia - high blood glucose (BG). Patients must also be careful not to inject too much insulin because this could induce hypoglycemia (low BG), which can potentially be fatal. Patients therefore follow a …
New method for multivariate distribution regression using NPT metric.
problem Regression with multivariate distributional responses and Euclidean predictors.
method Fréchet regression with nonparanormal transport (NPT) metric.
result Efficient estimation and granular interpretation of predictor effects.
In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulationcs.LG
Modern treatments for Type 1 diabetes (T1D) use devices known as artificial pancreata (APs), which combine an insulin pump with a continuous glucose monitor (CGM) operating in a closed-loop manner to control blood glucose levels. In practice, poor performance of APs (frequent hyper- or hypoglycemic events) is common en…
Most existing algorithms for dictionary learning assume that all entries of the (high-dimensional) input data are fully observed. However, in several practical applications (such as hyper-spectral imaging or blood glucose monitoring), only an incomplete fraction of the data entries may be available. For incomplete sett…
\begin{abstract} We model individual T2DM patient blood glucose level (BGL) by stochastic process with discrete number of states mainly but not solely governed by medication regimen (e.g. insulin injections). BGL states change otherwise according to various physiological triggers which render a stochastic, statisticall…
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain…
Study discovers patterns in insulin needs for T1D patients.
problem Finding the right insulin dose and time for T1D patients is challenging.
method Used OpenAPS Data Commons dataset and time series techniques like matrix profile and multi-variate clustering.
result Identified temporal patterns in insulin needs driven by factors like carbohydrates and possibly others.
Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
problem Reduce communication frequency in networked AP systems while maintaining control performance.
method Develops a DRL-based controller that avoids explicit update timing learning, using a semi-Markov decision process (SMDP).
result Improves communication efficiency without sacrificing control performance.
This paper explores the impact of metric choice on Fréchet regression.
problem Choosing the right metric for Fréchet regression in complex data.
method Review and extensive numerical studies of existing dimension reduction methods.
result Different metrics significantly affect the estimation of central and central mean space.
Paper presents estimators for entropy and information in probabilistic models.
problem Estimating entropy and mutual information in high dimensions is challenging.
method EEVI uses importance sampling with proposal distributions like amortized variational inference and sequential Monte Carlo.
result EEVI delivers accurate upper and lower bounds on information quantities.
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
problem Accurate prediction of all-cause in-hospital mortality in healthcare.
method Comprehensive feature engineering approach using vital signs, laboratory results, and demographic data.
result Random Forest model achieved highest performance with AUC of 0.94, significantly outperforming other models.
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
problem Lack of reliable inference for ML-discovered nonlinearities and interactions in epidemiological data.
method Combines Bayesian sparse regression, tree ensembles, and Shapley values.
result Valid uncertainty quantification for feature effects at the individual level.
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best healthcare possible for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time an…
Paper uses RL to optimize daily step distribution for better health biomarkers.
problem Lack of personalized PA distribution recommendations for health biomarkers.
method Developed an offline reinforcement learning algorithm to learn optimal PA distributions.
result Learned optimal policy suggests more consistent daily steps and tailored recommendations.
New model identifies regimes in non-stationary data.
problem Identifying latent regimes in non-stationary systems with instantaneous effects.
method Identifiable Markov Switching Models with exponential family noise.
result Established identifiability of latent regimes and causal structures.
Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopiesstat.AP
Proposes a method for training Bayesian neural networks using synthetic data from Raman and CARS spectra.
problem Limited real observations in Raman and CARS spectroscopy.
method Log-Gaussian Gamma Processes and Bayesian Neural Networks.
result Trained Bayesian neural networks provide accurate estimates of Raman and CARS spectra with uncertainty quantification.
The paper proposes a method to assess surrogate heterogeneity in non-randomized data.
problem Lack of methods to evaluate surrogate heterogeneity in non-randomized data.
method Proposes a framework using meta-learners to assess surrogate heterogeneity in real-world data.
result Identifies individuals for whom the surrogate is a valid replacement of the primary outcome.
Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Densityq-bio.QM
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
Method predicts NAFLD risk with high accuracy and distribution-free coverage guarantees.
problem Insufficient population-level screening tools for NAFLD.
method Gradient-boosted decision trees with conformal prediction.
result Method achieves AUROC of 0.912 internally and 0.891 externally, superior to other models.
Method minimizes total cost of classification by acquiring covariates efficiently.
problem Minimizing total cost of classification in applications with covariate acquisition costs.
method Formalizes optimization goal using Bayes risk, introduces assumptions for computable solution.
result Proposed method achieves lowest total costs compared to previous methods on medical datasets.
Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to …
With the increasing availability of wearable devices, continuous monitoring of individuals' physiological and behavioral patterns has become significantly more accessible. Access to these continuous patterns about individuals' statuses offers an unprecedented opportunity for studying complex diseases and health conditi…
Objective: To evaluate unsupervised clustering methods for identifying individual-level behavioral-clinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials and Methods: We used hierarchical clustering (HC) to identify groups of meals with simila…
Many sequential decision-making tasks require choosing at each decision step the right action out of the vast set of possibilities by extracting actionable intelligence from high-dimensional data streams. Most of the times, the high-dimensionality of actions and data makes learning of the optimal actions by traditional…
CBDL uses credal sets to improve uncertainty quantification in deep learning.
problem Uncertainty in predictions and robustness to distribution shifts in deep learning.
method Train an infinite ensemble of Bayesian Neural Networks using credal sets.
result CBDL distinguishes between aleatoric and epistemic uncertainties and quantifies them better than single BNNs.