Endocrinology

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

INTRODUCTION: As one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions of people and puts an enormous financial burden on the US economy. We aimed to develop predictive models to identify risk factors for type 2 diabetes, which could help facilitate early diagnosis and intervention and also reduce medical costs.

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...

Optimization of Growth Medium and Fermentation Conditions for the Production of Laccase3 from Using Recombinant .

Statistical experimental methods were used to optimize the medium for mass production of a novel laccase3 (Lac3) by recombinant TYEGLAC3-1. The basic...

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...

Radiation-Induced Hypothyroidism After Radical Intensity Modulated Radiation Therapy for Oropharyngeal Carcinoma.

PURPOSE: To evaluate 2 published normal tissue complication probability models for radiation-induced hypothyroidism (RHT) on a large cohort of orophar...

CT Texture Analysis and Machine Learning Improve Post-ablation Prognostication in Patients with Adrenal Metastases: A Proof of Concept.

INTRODUCTION: To assess the performance of pre-ablation computed tomography texture features of adrenal metastases to predict post-treatment local pro...

Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks.

Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign an...

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),...

NOVEL MUTATIONS IN AN INFANT WITH MICROCEPHALIC PRIMORDIAL DWARFISM, DILATED CARDIOMYOPATHY, SUBCLINICAL HYPOTHYROIDISM, AND EARLY DEATH: EXPANDING THE PHENOTYPE OF MUTATIONS.

OBJECTIVE: Microcephalic primordial dwarfism (MPD) is a group of clinically and genetically heterogeneous disorders which result in severe prenatal an...

Prediction of Immunohistochemistry of Suspected Thyroid Nodules by Use of Machine Learning-Based Radiomics.

The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected...

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...

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.,...

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 ...

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...

Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.

PURPOSE: New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We...

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...

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...