An Explainable Machine Learning Model for Predicting Short‐Term Haemodynamic Changes Post‐TIPS With Prognostic Implications

While remeasuring portacaval pressure gradient (PPG) after transjugular intrahepatic portosystemic shunt (TIPS), it provides superior prognostic information, and its clinical utility is limited by invasiveness. We aimed to develop and validate an explainable machine learning (ML) model for predicting short‐term PPG changes and improving prognostic value of PPG in cirrhotic patients undergoing TIPS.