Diabetes

Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model.

Management of diabetes requires a multifaceted approach of risk factor reduction; through management of risk factors such as glucose, blood pressure and cholesterol. Goals for these risk factors often vary and guidelines suggest that this is based on patient characteristics and need to be individualized. Evaluating risk is therefore critically important to determine goals and choose appropriate tr...

Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods.

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping...

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular dise...

Temporal deep learning framework for retinopathy prediction in patients with type 1 diabetes.

The adoption of electronic health records in hospitals has ensured the availability of large datasets that can be used to predict medical complication...

A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet.

Ultrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morpholog...

A Prediction Algorithm for Hypoglycemia Based on Support Vector Machine Using Glucose Level and Electrocardiogram.

A prediction algorithm for hypoglycemic events is proposed using glucose levels and electrocardiogram (ECG) with support vector machine (SVM). We extr...

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices.

Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision los...

SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging.

Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, ...

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.

We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross...

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.

In this study, we provide a systems biology method to investigate the carcinogenic mechanism of oral squamous cell carcinoma (OSCC) in order to identi...

Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple leve...

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could s...

Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction.

BACKGROUND AND AIMS: Healthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machin...

IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation.

BACKGROUND AND OBJECTIVE: Tiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropath...

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

BACKGROUND: To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomogr...

Deep Learning Classification of Treatment Response in Diabetic Painful Neuropathy: A Combined Machine Learning and Magnetic Resonance Neuroimaging Methodological Study.

Functional magnetic resonance imaging (fMRI) has been shown successfully to assess and stratify patients with painful diabetic peripheral neuropathy (...

Novel Internet of Things based approach toward diabetes prediction using deep learning models.

The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medi...

Diabetic retinopathy screening using deep learning for multi-class imbalanced datasets.

Screening and diagnosis of diabetic retinopathy disease is a well known problem in the biomedical domain. The use of medical imagery from a patient's ...

Deep learning fuzzy immersion and invariance control for type-I diabetes.

In this study, a novel approach is proposed for glucose regulation in type-I diabetes patients. Unlike most studies, the glucose-insulin metabolism is...

Feasibility Study of Constructing a Screening Tool for Adolescent Diabetes Detection Applying Machine Learning Methods.

Prediabetes and diabetes are becoming alarmingly prevalent among adolescents over the past decade. However, an effective screening tool that can asses...