An AI-based multi-step model for surgical timing in pediatric oncology

Summary. This study presents a two-phase AI-based model to predict surgical wait times in paediatric oncology patients. Using real-world data from 1478 patients and 6145 surgeries, the model first classifies surgical urgency, then estimates wait times for urgent cases. Random Forest emerged as the best-performing algorithm in both phases, and SHAP analysis identified similar key predictive features. Results support AI’s role in improving surgical planning, resource allocation, and clinical decision-making.