Find out how machine learning identifies country-specific health system drivers shaping global cancer survival and highlights ...

Recent progress in survival analysis has been driven by the integration of machine learning techniques with traditional statistical models, such as the Cox proportional hazards model. This synthesis ...

Machine learning models showed strong predictive performance for 5-year survival in stage III colorectal cancer patients, with AUC values between 0.766 and 0.791. Key prognostic factors identified ...

Researchers have turned artificial intelligence into a powerful new lens for understanding why cancer survival rates differ ...

Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, ...

Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records Data collected in the multicentric PRAIS ...

A UCLA-led team has developed a machine-learning model that can predict with a high degree of accuracy the short-term survival of dialysis patients on Continuous Renal Replacement Therapy (CRRT). CRRT ...

AI has revealed why cancer survival differs so dramatically around the world, highlighting the specific health system factors that matter most in each country.