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 ...
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 ...
Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and ...