PURPOSE: The aim of this study was to develop indices of feigning spectrum behaviour (FSB) on the Visual Analogue Pain Scale (VAS) Neck Disability Index (NDI) and Impact of Events Scale (IES) in people with whiplash associated disorder (WAD) after mo...
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...
This study investigates the developmental status and influencing factors of artificial intelligence (AI) literacy and computational thinking (CT) literacy among undergraduates in China's "four new" majors. Guided by the Technology Acceptance Model, S...
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...
This study introduces a non‑invasive approach for neurovisual classification of geometric shapes by capturing and decoding laser‑speckle patterns reflected from the human striate cortex. Using a fast digital camera and deep neural networks (DNN), we ...
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...
Schizophrenia is a complex psychiatric disorder that disrupts cognition, emotions, and social behavior. Timely and accurate diagnosis is essential for effective treatment. Traditional diagnostic methods relying on clinical assessments have limitation...
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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