Browsing: predicts

MACHINE learning models demonstrated moderate performance in predicting acute and late radiation-induced skin reactions in patients with locally advanced rectal cancer receiving neoadjuvant chemoradiotherapy. The retrospective analysis included 358 patients and integrated clinical, anatomical, haematological and dosimetric data.

ACTIVATED clotting time prediction using machine learning achieved strong performance in patients with atrial fibrillation undergoing catheter ablation, with a deep neural network model outperforming five alternative algorithms in a retrospective analysis of 1,144 cases.

MACHINE learning was used to predict moderate to severe acute kidney injury (AKI) in patients with acute pancreatitis admitted to intensive care, with an XGBoost model achieving area under the curve values of 0.850 in internal validation and 0.702 in external validation.