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 ...
A machine learning model enhances treatment decisions for hepatocellular carcinoma, optimizing survival outcomes through personalized risk stratification.
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 ...
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 ...
Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...
Researchers have identified multiple causal biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD), ...
Chemists have created a machine learning tool that can identify the chemical composition of dried salt solutions from an image with 99% accuracy. By using robotics to prepare thousands of samples and ...
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