Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machin...
The rising prevalence of cardiometabolic multimorbidity (CMM), characterized by the coexistence of two or more cardiometabolic disorders, poses a significant public health challenge in aging populations. While ambient air pollution is a recognized en...
Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, ...
The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variabilit...
BACKGROUND: Effective communication is fundamental to high-quality health care delivery, influencing patient satisfaction, adherence to treatment plans, and clinical outcomes. However, communication skills training for medical undergraduates often fa...
Assessing the efficacy of radiotherapy in patients with high-grade gliomas (HGGs) is challenging due to the occurrence of pseudo-progression and radionecrosis. This study introduces a directed graph network leveraging MR image features at multiple ti...
OBJECTIVE: Mild cognitive impairment (MCI) signals cognitive decline beyond normal aging and increases dementia risk. Early identification enables preventative interventions, yet many patients in primary care go undetected. This study examines whethe...
Early identification of students' mental health issues has become an urgent priority in education and public health. However, existing studies often rely on questionnaire-based assessments or traditional machine learning models, which are limited by ...
Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to convention...
BACKGROUND AND PURPOSE: This study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.
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