BACKGROUND: Atherosclerotic cardiovascular disease poses a heavy burden on the population's health and health care costs. Identifying apparently healthy individuals at risk of developing cardiovascular diseases using clinical prediction models raises...
BACKGROUND: Artificial intelligence (AI) has demonstrated transformative potential in the health care field; yet, its clinical adoption faces challenges such as inaccuracy, bias, and data privacy concerns. As the primary operators of AI systems, phys...
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common cause of chronic liver disease worldwide, affecting over 30% of the global general population. Its progressive nature and association with other chronic diseases make...
To enhance user interaction experience in furniture customization, this study optimizes an Internet of Things (IoT)-driven Artificial Intelligence (AI)-assisted design system. First, the study analyzes human-computer interaction theories in IoT envir...
BACKGROUND: Despite the National Health Insurance (NHI) system implemented in South Korea, concerns persist regarding access to health coverage for low-income households. To address this issue, this study aims to use machine learning-based data minin...
Diabetic kidney disease (DKD) progression is not well understood. Using high-throughput proteomics, biostatistical, pathway and machine learning tools, we examine the urinary Complement proteome in two prospective cohorts with type 1 or 2 diabetes an...
This study aimed to develop a reliable and explainable predictive model for illicit drug use (IDU). The model uses a machine learning (ML) algorithm to predict IDU using hematological differences between illicit drug users (IDUr) and non-users (n-IDU...
This study investigated the effects of feature augmentation, which uses generated images with specific imaging features, on the performance of isocitrate dehydrogenase (IDH) mutation prediction models in gliomas. A total of 598 patients were included...
The mortality rates have been increasing for glioma in adolescents and young adults (AYAs, aged 15-39 years). However, current biomarkers for clinical assessment in AYAs glioma are limited, prompting the urgent need for identifying ideal prognostic s...
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