BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...
BACKGROUND: Residual cardiovascular risk persists in patients with hypercholesterolemia despite lipid-lowering therapy, underscoring the importance of inflammation in ASCVD development. This study evaluated the relationship between Systemic Inflammat...
Recent studies have demonstrated that for various diseases, incorporating polygenic risk scores (PRSs) for other traits and diseases into the PRS-based risk prediction model may improve predictive performance - known as Multiple Polygenic Score (MPS)...
Acute kidney injury is a common and severe complication following total hip arthroplasty, particularly in elderly or high-risk patients with chronic conditions, significantly increasing morbidity and mortality rates. Traditional prediction methods of...
This study explores the use of radiomic features extracted from preoperative T2-weighted MRI and CT images, combined with machine learning models, to predict the risk of vertebral refracture after percutaneous kyphoplasty (PKP) in postmenopausal wome...
INTRODUCTION: Hypertension is a leading contributor to maternal and cardiometabolic morbidity in Bangladesh. We developed and interpreted machine-learning (ML) models to predict hypertension and rank associated factors among married women with the go...
BACKGROUND: Life's Essential 8 (LE8) is a comprehensive cardiovascular health (CVH) metric that is associated with chronic diseases. This study aimed to investigate the association between LE8 and diabetic retinopathy (DR) and the mediating role of d...
BACKGROUND: Breast cancer is one of the most prevalent malignancies globally, imposing a substantial disease burden. Its inherent heterogeneity complicates prognosis and treatment, underscoring the need for accurate survival prediction models to guid...
INTRODUCTION: To enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) al...
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...
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