Predicting the Outcome of Ischemic Hepatitis with Real-Patient Data Using Machine Learning Tools

Ischemic hepatitis (IH) results from shock-related conditions that impair oxygenated blood flow to the liver, causing hepatocyte death. Diagnosis relies largely on clinical history due to the absence of specific diagnostic tests and limited ability to predict outcomes. This study applies machine learning methods to real-world IH patient data to improve outcome prediction. Biomedical indicators analyzed include creatinine, international normalized ratio (INR), aspartate aminotransferase (AST), alanine transaminase (ALT), and bilirubin. Data were collected from multiple U.S. centers through the Acute Liver Failure Study Group (ALFSG), a multicenter network focused on this rare condition. We implemented logistic regression, regression tree methods (including random forests and Bayesian Additive Regression Trees), and neural networks. To enhance model performance, we applied Synthetic Minority Oversampling Technique (SMOTE), along with optimization and feature selection techniques. Results show that INR and bilirubin are consistently selected as key predictors across all models, which demonstrated comparable predictive performance.