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.
Modello multi-step basato su intelligenza artificiale per il timing chirurgico in oncologia pediatrica / Capuzzi, Silvia; Baldisseri, Federico; Cacchione, Antonella; Carai, Andrea; Fabozzi, Francesco; Pietrabissa, Antonio; Mastronuzzi, Angela; Tozzi, Alberto Eugenio; Ferro, Diana. - In: RECENTI PROGRESSI IN MEDICINA. - ISSN 2038-1840. - 116:10(2025), pp. 593-594. [10.1701/4573.45791]
Modello multi-step basato su intelligenza artificiale per il timing chirurgico in oncologia pediatrica
Capuzzi, Silvia
;Baldisseri, Federico;Pietrabissa, Antonio;
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
