Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk ...
Researchers sought to determine an effective approach to predict postembolization fever in patients undergoing TACE.
A machine learning model predicted cardiac tamponade during AF ablation with high accuracy. Learn how XGBoost may improve ...
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Physical function metrics improve mortality prediction in elderly heart failure patients
Current models of mortality risk after heart failure (HF) rely primarily on cardiac-specific clinical variables and may ...
Interpretable AI model could offer new insights into why medicines cause certain side effects, helping to improve future drug safety predictions.
The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 ...
Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...
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Machine learning model predicts serious transplant complications months before symptoms appear
A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications after stem cell and bone marrow transplants, according to new research ...
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 ...
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