Study aims to predict meal-to-meal blood glucose for T1D patients.
problem Accurately predict future blood glucose levels from current features.
method Applied machine learning algorithms to a new T1D dataset.
result Most accurate predictor achieved an errL1 loss of 2.70 mmol/L.
Smartphone data shows promise but accuracy issues remain.
problem Inaccurate travel surveys from smartphone data.
method Training algorithms on data quality and validating labels.
result Machine learning methods are limited by data quality.
Deep learning system improves diabetic retinopathy and macular edema grading.
problem Manual screening of diabetic retinopathy and macular edema images is labor-intensive and error-prone.
method Used deep learning on fundus images, achieving comparable or better performance than previous studies.
result Deep learning system can accurately classify images according to clinical grading scales.
This paper provides a ML framework for diabetes prediction and care management.
problem Diabetes prediction and care management challenges in real-world healthcare.
method Illustrates a Machine Learning framework for T2DM prediction and risk stratification.
result ML models align with physician's disease management steps.
Deep learning detects diabetic retinopathy stages from single fundus photos.
problem Early detection of diabetic retinopathy for treatment success.
method Convolutional neural networks (CNN) for automatic stage detection.
result Sensitivity and specificity of 0.99 on APTOS 2019 Blindness Detection Dataset.
Study predicts Type 2 diabetes with 85% accuracy using machine learning.
problem Early detection of Type 2 diabetes improves patient health outcomes.
method Used machine learning algorithms and a weighted ensemble model on patient data.
result Ensemble model achieved 85% accuracy in predicting Type 2 diabetes.
Health professionals can use natural language processing (NLP) technologies when reviewing electronic health records (EHR). Machine learning free-text classifiers can help them identify problems and make critical decisions. We aim to develop deep learning neural network algorithms that identify EHR progress notes perta…
CopulaSMOTE addresses class imbalance in diabetes prediction models.
problem Class imbalance in diabetes prediction models, especially with fewer confirmed cases.
method Copula-based oversampling approach that models joint dependence structure.
result CopulaSMOTE improves minority-class recovery in larger diabetes datasets.
New method interprets deep embeddings for diabetes patient clustering.
problem Interpreting deep embeddings for disease progression.
method Patient clustering approach using deep embeddings.
result Clinically meaningful insights into diabetes progression patterns.
This study investigates transfer learning for medical image classification.
problem Limited data for training deep neural networks in medical domains.
method Transfer learning using various DNNs for diabetic retinopathy and macular edema.
result Transfer learning is feasible and promising for medical image classification.
Reinforcement Learning improves insulin bolus decisions for type-I diabetes patients.
problem Optimal insulin bolus decisions for type-I diabetes patients are not well-established.
method Applied Reinforcement Learning to simulated T1DM data.
result Optimal bolus rule differs from standard advisors and can prevent hypoglycemia.
Novel framework detects CKD in diabetic patients using sparse EHR representations.
problem Early detection of CKD in diabetic patients.
method Sparse longitudinal representations of EHR data.
result Proposed model achieves higher predictive performance than baselines.
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.
The paper examines how calibration affects the interpretability of ML models in diabetes screening.
problem Interpreting complex ML models in healthcare, especially in diabetes screening.
method Examined the impact of model calibration on interpretability using three visualization techniques.
result Calibrated models provide clearer cause-effect relationships in ML predictions.
Paper develops NN models for diabetes screening using NHANES data.
problem Developing accurate predictive models for diabetes in diverse populations.
method Proposes a neural network framework with survey weights, uncertainty quantification.
result Robust risk score models for diabetes in US population.
Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such …
Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate…
Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a larg…
Deep neural network predicts diabetic readmission with high accuracy.
problem Predicting 30-day readmission for diabetic patients.
method Categorical embeddings and deep neural network.
result 95.2% accuracy and 97.4% AUROC on diabetic readmission data.
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.
Prediction of disease onset from patient survey and lifestyle data is quickly becoming an important tool for diagnosing a disease before it progresses. In this study, data from the National Health and Nutrition Examination Survey (NHANES) questionnaire is used to predict the onset of type II diabetes. An ensemble model…
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.
AI virtual doctor predicts diabetes from non-invasive data.
problem Limited access to primary medical care in rural areas.
method Interactive AI system with speech recognition and synthesis, deep neural networks.
result System accurately predicts type 2 diabetes.
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
CNNs help diagnose diabetic retinopathy by localizing lesions.
problem Diabetic retinopathy diagnosis requires identifying lesions in fundus images.
method Post-attention technique (Grad-CAM) on deep learning models' penultimate layer.
result InceptionV3 model achieves best performance and localizes lesions better.
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.
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags. This paradigm is particularly suitable in contexts where providing individual labels is expensive and label aggregates are more easily obtained. In the healthcare…
Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning…
Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to translate RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach …
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.
Generative model predicts daily activity sequences with duration-aware dynamics.
problem Accurately forecasting granular daily activity sequences for energy demand.
method Hierarchical semi-Markov models with duration-aware dynamics.
result Explicitly modeling activity durations improves predictive performance.
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.
OTRE uses OT to improve retinal images, outperforming existing methods.
problem Improving quality of non-mydriatic retinal images for accurate diagnoses.
method OT theory for image-to-image translation, regularization by enhancing.
result OTRE outperforms state-of-the-art methods on various retinal image tasks.
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.
25% of people who received a liver transplant will go on to develop diabetes within the next 5 years. These thousands of individuals are at 2-fold higher risk of cardiovascular events, graft loss, infections, as well as lower long-term survival. This is partly due to the medication used during and/or after transplant t…
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection…
Dual-edge spatial Jacobian image graph for interpretable diabetic retinopathy grading
problem Automated diabetic retinopathy grading from color fundus photographs
method Dual-edge spatial-Jacobian image graph
result 0.8076 accuracy, 0.8312 quadratic weighted kappa, 0.5915 macro-F1, 0.9330 adjacent-grade accuracy
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.
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.
RETINA Benchmark evaluates Bayesian deep learning on diabetic retinopathy detection.
problem Reliable uncertainty quantification for deep learning models in medical applications.
method Design and evaluation of a real-world diabetic retinopathy dataset and tasks.
result Benchmarking of Bayesian deep learning methods on diabetic retinopathy detection tasks.
Deep learning system tracks stool consistency for GI disease assessment.
problem Lack of objective stool consistency measurements in chronic GI disease.
method Computer vision and deep convolutional neural networks (CNN).
result Developed a stool detection and tracking system.
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.
Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-…
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…
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed us…
Deep models improve GWAS by identifying genetic interactions.
problem Missing non-linear interaction effects in GWAS.
method Gradient-based DeepLIFT technique to interpret deep models.
result Known and novel genetic risk factors identified.
Proposes a copula-based filter for diabetes risk prediction.
problem Feature selection for robust and interpretable predictive modeling in medicine, especially for extreme patient strata.
method Copula-based supervised filter using Gumbel-copula implied upper-tail concordance score (lambda U).
result The proposed filter outperforms standard filters and provides clinically coherent predictors.
Study predicts diabetes biomarkers using wearable data.
problem Understanding and predicting diabetes progression.
method Wide and deep neural network with LSTM structure.
result Model predicts biomarkers with low error rates.