BACKGROUND: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event predic...
BACKGROUND: The ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image c...
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattention. This study aims to develop a verifiable and interpretable machine learning model to identify ADHD...
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.
OBJECTIVE: Blood pressure fluctuations during dialysis, including intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN), are common complications among patients undergoing maintenance hemodialysis. Early prediction of IDH and IDHTN c...
In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms h...
Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney biopsy is unsuitable for regular screening, and imaging tests are costly and time-consuming. Several st...
Cervical spinal cord injury is often catastrophic, frequently leading to irreversible impairment. MRI has become the gold standard for evaluating spinal cord injuries (SCI). Our study aimed to assess the accuracy of a radiomics approach, based on mac...
Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolution...
Performance-related indicators are crucial for evaluating and forecasting performance, enhancing decision-making efficiency, and establishing sustainable growth strategies. They motivate individuals and organizations, increase transparency, and accur...
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