Asthma is a chronic respiratory disease characterized by wheezing and difficulty breathing, which disproportionally affects 4.7 million children in the U.S. Currently, there is a lack of asthma predictive models for youth with good performance. This ...
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 ...
Electrocorticography (ECoG) signals provide a valuable window into neural activity, yet their complex structure makes reliable classification challenging. This study addresses the problem by proposing a feature-selective framework that integrates mul...
OBJECTIVE: Age-related macular degeneration (AMD) is a retinal disorder that significantly impairs vision. This study investigates various machine learning models for predicting AMD risk, laying the groundwork for further research using big data and ...
AIM: Visual-motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children's VMI developmental status.
Accurate survival prediction is essential for guiding follow-up strategies in patients with cT1b renal cell carcinoma (RCC). Traditional AJCC TNM staging systems provide limited prognostic accuracy. Data from the SEER database were used, which includ...
Modeling and analysis of the lyophilization process for low-temperature drying of pharmaceutical compounds was evaluated via a hybrid model that combines mass transfer and machine learning. We investigated the predictive accuracy of three machine lea...
Student stress in higher education remains a pervasive problem, yet many institutions lack affordable, scalable, and interpretable tools for its detection and management. Existing methods frequently depend on costly physiological sensors and opaque m...
BMC medical informatics and decision making
Oct 31, 2025
BACKGROUND: Labor- and cost-intensive manual chart review of Electronic Health Records (EHRs) remains a major bottleneck in retrospective studies, particularly when rare-disease cohorts require high specificity. Automated Natural Language Processing ...
Automated brain tumor detection represents a fundamental challenge in contemporary medical imaging, demanding both precision and computational feasibility for practical implementation. This research introduces a novel Vision Transformer (ViT) framewo...
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