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.
Capuzzi, S., Baldisseri, F., Cacchione, A., Carai, A., Fabozzi, F., Pietrabissa, A., Mastronuzzi, A., Tozzi, A. E., Ferro, D., An AI-based multi-step model for surgical timing in pediatric oncology, <<RECENTI PROGRESSI IN MEDICINA>>, 2025; 116 (10): 593-594. [doi:10.1701/4573.45791] [https://hdl.handle.net/10807/329558]
An AI-based multi-step model for surgical timing in pediatric oncology
Carai, AndreaMembro del Collaboration Group
;Mastronuzzi, AngelaConceptualization
;
2025
Abstract
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.
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