BACKGROUND: Infective endocarditis (IE) carries high in-hospital mortality, particularly among intensive care unit (ICU) patients. The predictive role of blood culture positivity in these patients remains unclear.
BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a major contributor to cardiovascular mortality, yet reliable tools for individualized mortality prediction remain limited. Machine learning offers the potential to enhance pr...
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...
BACKGROUND: The Atherogenic Index of Plasma (AIP) is a novel logarithmic index that combines fasting triglyceride and high-density lipoprotein cholesterol (HDL-C) concentrations and is associated with the burden of atherosclerosis. Currently, the non...
BACKGROUND: Atrial fibrillation (AF) is a major cardiovascular issue in critically ill patients, linked to elevated mortality rates. The Stress Hyperglycemia Ratio (SHR), a novel metric of glucose control, has shown promise in predicting adverse outc...
BACKGROUND: Patients with rare diseases often face prolonged diagnostic journeys due to the low prevalence and diverse clinical presentations of these conditions. In Germany, specialized centers for rare diseases, established at university hospitals,...
BACKGROUND: Mild cognitive impairment (MCI) represents a transitional stage to Alzheimer's disease (AD), making progression prediction crucial for timely intervention. Predictive models integrating clinical, laboratory, and survival data can enhance ...
Accurate detection and classification of clinically significant prostate cancer remain critical challenges in medical imaging. Despite numerous studies focusing on feature extraction and classification, none have systematically assessed the impact of...
Diabetes mellitus is a major global health burden, and early identification of insulin dependency is important for timely intervention. This study developed an artificial intelligence-based diagnostic system using a real-world clinical dataset of 100...
We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the ...
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