Primary Care

A Study on Prevalence and Factors Affecting Hypertension in an Iranian Population: Results from the Fasa Cohort Study.

BACKGROUND: In recent years, hypertension has been one of the most important noncommunicable diseases worldwide. In this context, identifying the predictors of this disease can help health policymakers to reduce its burden. This study aimed to identify some of the most important influential factors of hypertension and present a model to predict this disease in the data from a large sample cohort s...

Knee Osteoarthritis SCAENet: Adaptive Knee Osteoarthritis Severity Assessment Using Spatial Separable Convolution with Attention-Based Ensemble Networks with Hybrid Optimization Strategy.

Osteoarthritis (OA) of the knee is a chronic state that significantly lowers the quality of life for its patients. Early detection and lifetime monitoring of the progression of OA are necessary for preventive therapy. In the course of therapy, the Kellgren and Lawrence (KL) assessment model categorizes the rigidity of OA. Deep techniques have recently been used to increase the precision and effect...

Pre-training strategy for antiviral drug screening with low-data graph neural network: A case study in HIV-1 K103N reverse transcriptase.

Graph neural networks (GNN) offer an alternative approach to boost the screening effectiveness in drug discovery. However, their efficacy is often hin...

A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals wit...

Development of a machine learning model for precision prognosis of rapid kidney function decline in people with diabetes and chronic kidney disease.

AIMS: To develop a machine learning model for predicting rapid kidney function decline in people with type 2 diabetes (T2D) and chronic kidney disease...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism.

BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to differen...

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These tools employ active learning, where algorithms so...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated ...

Current status and dilemmas of osteoporosis screening tools: A narrative review.

OBJECTIVE: This review aims to explore the strengths and dilemmas of existing osteoporosis screening tools and suggest possible ways of optimization, ...

Machine learning-based risk prediction for major adverse cardiovascular events in a Brazilian hospital: Development, external validation, and interpretability.

BACKGROUND: Studies of cardiovascular disease risk prediction by machine learning algorithms often do not assess their ability to generalize to other ...

Modeling health risks using neural network ensembles.

This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a he...

Explainable Machine-Learning Models to Predict Weekly Risk of Hyperglycemia, Hypoglycemia, and Glycemic Variability in Patients With Type 1 Diabetes Based on Continuous Glucose Monitoring.

BACKGROUND AND OBJECTIVE: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM)...

GloGen: PPG prompts for few-shot transfer learning in blood pressure estimation.

With the rapid advancements in machine learning, its applications in the medical field have garnered increasing interest, particularly in non-invasive...

Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study.

AIMS: This study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in ...

The Bioprotective Effects of Marigold Tea Polyphenols on Obesity and Oxidative Stress Biomarkers in High-Fat-Sugar Diet-Fed Rats.

The research is aimed at exploring the potential of marigold petal tea (MPT), rich in polyphenol contents, against oxidative stress and obesity in a ...

Machine learning and deep learning-based approach to categorize Bengali comments on social networks using fused dataset.

Through the advancement of the contemporary web and the rapid adoption of social media platforms such as YouTube, Twitter, and Facebook, for example, ...

iPSC-derived and Patient-Derived Organoids: Applications and challenges in scalability and reproducibility as pre-clinical models.

Recent advancements in stem cell technology have led to the development of organoids - three-dimensional (3D) cell cultures that closely mimic the str...

Employing Machine Learning Models to Predict Potential α-Glucosidase Inhibitory Plant Secondary Metabolites Targeting Type-2 Diabetes and Their Validation.

The need for new antidiabetic drugs is evident, considering the ongoing global burden of type-2 diabetes mellitus despite notable progress in drug dis...

Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curativ...