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 ...

Unraveling survival disparities in primary central nervous system (CNS) lymphoma: An analysis of race, socioeconomic factors, and treatment outcomes using the Surveillance, Epidemiology, and End ...

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 ...

A machine learning model enhances treatment decisions for hepatocellular carcinoma, optimizing survival outcomes through personalized risk stratification.

In oncology and cardiology, AI’s strength lies in deep learning techniques applied to imaging and genomic data. Convolutional ...

PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology We used an innovative machine learning approach to analyze ...

AI-powered analysis of routine blood tests can reveal hidden patterns that predict recovery and survival after spinal cord ...

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 ...

Researchers have identified multiple causal biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD), ...