Yoshua Bengio, considered by many to be one of the godfathers of AI, has long been at the forefront of machine-learning ...

We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance ...

Donation after circulatory death (DCD) procurements provide an opportunity to alleviate the limited organ supply for solid ...

Researchers successfully developed a machine learning-based method for predicting symptom deterioration in patients with cancer.

The aim is to find new uses of real-world data in AI development, and to help health systems move from fragmented, compliance ...

The CT-based whole-lung radiomic nomogram accurately identifies AECOPD and offers a robust tool for clinical diagnosis and treatment planning.

Integrating AI models with the medical domain is challenging as it involves more complex workflows compared to traditional machine learning operations (MLOps). Requirements such as model performance ...

Learn how to choose the right cross-validation method for your machine learning projects. Compare techniques like k-fold, ...

Objective: This study aimed to develop and evaluate a machine learning (ML)–based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under ...

1 Department of Computer Science, College of Informatics, National Chengchi University, Taipei, Taiwan 2 College of Education, National Chengchi University, Taipei, Taiwan Randomization is a standard ...

Abstract: Asthma, a prevalent chronic respiratory condition, poses significant challenges to public health worldwide. Accurate and early prediction of asthma risk can greatly aid healthcare ...

The final, formatted version of the article will be published soon. Background: The rising global demand for total knee arthroplasty (TKA) has accelerated the shift toward ambulatory surgery, aimed at ...