This study reviews the common practices and procedures conducted to identify the cost
drivers that the past literature has classified into two main categories: qualitative and …

The emergent discipline of metabolomics has attracted considerable research effort in
hepatology. Here we review the metabolomic data for non-alcoholic fatty liver disease …

Background & Aims Microvascular invasion (MVI) impairs surgical outcomes in patients with
hepatocellular carcinoma (HCC). As there is no single highly reliable factor to preoperatively …

Early prediction of patient mortality risks during a pandemic can decrease mortality by
assuring efficient resource allocation and treatment planning. This study aimed to develop …

Objective To investigate whether machine learning-based analysis of MR radiomics can
help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa) …

Objective To evaluate the diagnostic performance of machine-learning based quantitative
texture analysis of CT images to differentiate small (≤ 4 cm) angiomyolipoma without visible …

Recently, support vector machine (SVM) has excellent performance on classification and
prediction and is widely used on disease diagnosis or medical assistance. However, SVM …

Large-scale metabolomics is a powerful technique that has attracted widespread attention in
biomedical studies focused on identifying biomarkers and interpreting the mechanisms of …

The classical linear multi-factor stock selection model is widely used for long-term stock
price trend prediction. However, the stock market is chaotic, complex, and dynamic, for …

Acylcarnitines play an essential role in regulating the balance of intracellular sugar and lipid
metabolism. They serve as carriers to transport activated long-chain fatty acids into …