BACKGROUND: Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial int...
Variable physiological [F]FDG uptake patterns and a lack of labelled data make it challenging to automatically distinguish normal from pathological suspicious uptake in whole-body PET/CT imaging. We propose a deep learning method that generates patie...
The classification of malignant versus benign microcalcifications in mammograms remains a critical yet challenging task in breast cancer screening. Deep learning models, particularly convolutional neural networks, have demonstrated promising results;...
Chimeric antigen receptor (CAR) T-cell therapy has shown significant success in treating diffuse large B-cell lymphoma (DLBCL). The initial step involves collecting autologous CD3 lymphocytes through apheresis, in which obtaining an adequate CD3 cell...
Emerging evidence links metabolic dysfunction-associated fatty liver disease (MAFLD) with increased all-cause and circulatory system disease (CSD) mortality in adults, yet survival machine learning studies are limited. This study analyzed 4415 NHANES...
Quantifying structural brain changes is critical for diagnosing and monitoring neurodegenerative diseases. Although magnetic resonance imaging (MRI) is the silver standard, limited accessibility and cost hamper routine use. We developed a deep learni...
Continuous renal replacement therapy (CRRT) is a vital intervention for critically ill patients with severe acute kidney injury, yet no standardized criteria exist to determine the optimal time for its discontinuation. We developed and validated mach...
OBJECTIVE: The lack of a rapid, validated, consistent test for tracking disease activity in patients with inflammatory bowel disease (IBD) is currently a major challenge. Currently used biomarkers have notable disadvantages, such as the slow processi...
OBJECTIVES: To develop a machine learning (ML)-based predictive model to determine the key predictors of dissatisfaction after occupational injury (OI).
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