A novel multi-task XGBoost model shows robust overall performance in predicting antimicrobial resistance in common gram-negative pathogens.

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...

Background Early graft failure within 90 postoperative days is the leading cause of mortality after heart transplantation. Existing risk scores, based on linear regression, often struggle to capture ...

The XGBoost model predicts hyperglycemia risk in psoriasis patients with high accuracy, achieving an AUC of 0.821 in the training set. A web-based calculator was developed to facilitate personalized ...

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...

Researchers have developed a novel AI-driven framework using the XGBoost algorithm to accurately evaluate the skid resistance of asphalt pavements under various conditions. Published in Smart ...