Diabetes

Machine Learning to Predict In-Hospital Morbidity and Mortality after Traumatic Brain Injury.

Recently, successful predictions using machine learning (ML) algorithms have been reported in various fields. However, in traumatic brain injury (TBI) cohorts, few studies have examined modern ML algorithms. To develop a simple ML model for TBI outcome prediction, we conducted a performance comparison of nine algorithms: ridge regression, least absolute shrinkage and selection operator (LASSO) reg...

Deep learning based retinal OCT segmentation.

We look at the recent application of deep learning (DL) methods in automated fine-grained segmentation of spectral domain optical coherence tomography (OCT) images of the retina. We describe a new method combining fully convolutional networks (FCN) with Gaussian Processes for post processing. We report performance comparisons between the proposed approach, human clinicians, and other machine learn...

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers.

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate...

Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.

OBJECTIVE: To develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabet...

Continuous Glucose Monitoring and Glycemic Control in Patients With Type 2 Diabetes Mellitus and CKD.

RATIONALE & OBJECTIVE: The accuracy of glycated hemoglobin (HbA) level for assessment of glycemic control in patients with chronic kidney disease (CKD...

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms.

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA),...

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.

OBJECTIVE: Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Curren...

Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives.

Artificial intelligence is playing an increasingly important role in many fields of medicine, assisting physicians in most steps of patient management...

Hyper-reflective foci segmentation in SD-OCT retinal images with diabetic retinopathy using deep convolutional neural networks.

PURPOSE: The purpose of this study was to automatically and accurately segment hyper-reflective foci (HRF) in spectral domain optical coherence tomogr...

The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes.

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g.,...

Study of the Application of Deep Convolutional Neural Networks (CNNs) in Processing Sensor Data and Biomedical Images.

The fast progress in research and development of multifunctional, distributed sensor networks has brought challenges in processing data from a large n...

Artificial Intelligence Approach To Investigate the Longevity Drug.

Longevity is a very important and interesting topic, and has been demonstrated to be related to longevity. We combined network pharmacology, machine ...

Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning.

Artificial intelligence (AI) is expected to support clinical judgement in medicine. We constructed a new predictive model for diabetic kidney diseases...

Strategies to Tackle the Global Burden of Diabetic Retinopathy: From Epidemiology to Artificial Intelligence.

Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in low- and middle-income countries...

An Intelligent Segmentation and Diagnosis Method for Diabetic Retinopathy Based on Improved U-NET Network.

Due to insufficient samples, the generalization performance of deep network is insufficient. In order to solve this problem, an improved U-net based i...

Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.

In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obta...

Hyper-G: An Artificial Intelligence Tool for Optimal Decision-Making and Management of Blood Glucose Levels in Surgery Patients.

BACKGROUND: Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may de...

Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis.

BACKGROUND: Spectral-domain optical coherence tomography (SDOCT) can be used to detect glaucomatous optic neuropathy, but human expertise in interpret...

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an ef...

Sex Differences in Diabetes Prevalence, Comorbidities, and Health Care Utilization among American Indians Living in the Northern Plains.

BACKGROUND: The American Indian (AI) population experiences significant diet-related health disparities including diabetes and cardiovascular disease ...