Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
Abstract: Automatic analysis methods of electrocardiograms (ECGs) usually required large-scale annotated training data, but the annotation process is extremely time-consuming. While semi-supervised ...
High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, ...
The ML model stratifies HCC patients by mortality risk, guiding treatment decisions between liver transplantation and surgical resection. The model demonstrated improved survival outcomes, with a 54% ...
The final, formatted version of the article will be published soon. Background): Diabetes Mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting ...
Abstract: The Broad Learning System (BLS) performs well in semi-supervised scenarios across many datasets, but its effectiveness is often limited by inadequate feature extraction and inefficient label ...
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