BACKGROUND: Glioblastoma (GBM) is no longer regarded as a single disease, as distinct molecular subgroups exist, with the mesenchymal (MES) having the worst prognosis. As such, there is a critical need for noninvasive methods to determine GBM molecul...
PURPOSE: Separating BPV from other positional nystagmus types can be challenging. We examined the utility of nystagmus slow-phase velocity (SPV) profiles when seeking to separate canalithiasis benign positional vertigo (BPV) from its mimics.
Task-based functional magnetic resonance imaging (fMRI) reveals individual differences in neural correlates of cognition but faces scalability challenges due to cognitive demands, protocol variability, and limited task coverage in large datasets. Her...
Surgical site infection (SSI) after colorectal cancer (CRC) surgery is still a significant healthcare issue. This study aimed to analyze risk factor associated with SSI. A total of 528 consecutive CRC patients who underwent curative resections betwee...
High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer's disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challenges for traditional classification method...
To evaluate the impact of various magnetic resonance imaging (MRI) preprocessing methods on radiomic feature reproducibility and classification performance in differentiating Parkinson's disease (PD) motor subtypes. We analyzed 210 T1-weighted MRI sc...
Hematological biomarkers have emerged as powerful tools in diagnosing Acute Heart Failure (AHF). This study introduces a novel diagnostic framework that integrates Explainable Artificial Intelligence (XAI) with Morris Sensitivity Analysis (MSA) to en...
BACKGROUND: Cancer survivorship is a complicated, chronic, and long-lasting experience, causing uncertainty and a wide range of physical and emotional health concerns. Due to the complexity of cancer, patients often seek out multiple sources of healt...
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...
OBJECTIVES: To develop and compare prediction models for 30-day and 1-year mortality in Heart failure with preserved ejection fraction (HFpEF) using EHR data, utilizing both traditional and machine learning (ML) techniques.
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