Explainable machine learning to predict postoperative ileus after radical cystectomy: an 11-year real-world cohort.

Background: Post-operative ileus (POI) is a frequent complication after radical cystectomy (RC). Conventional scores capture only linear relations and have limited accuracy. Interpretable machine learning (ML) may improve early risk stratification. Methods: In a single-centre real-world cohort ( Results: POI occurred in 28.9%. The back-propagation neural network performed best (AUC 0.828; accuracy 78.4%; Brier 0.143). Intra-operative nasogastric-tube placement and surgical approach dominated feature attribution, followed by medication history, lymph-node dissection, lymphocyte count and C-reactive protein. SHAP clarified feature effects and enabled interpretable, case-level risk summaries. Conclusion: An interpretable ML model based on routinely captured peri-operative variables accurately stratifies RC patients at risk for POI as early as postoperative day 0, outperforming existing nomograms and highlighting modifiable factors. Embedding this tool into electronic-health-record workflows could enable real-time alerts and risk-adapted management. Prospective multicentre validation is warranted.