儿科肿瘤手术时间的人工智能多步模型。

Q3 Medicine

Recenti progressi in medicina Pub Date : 2025-10-01 DOI:10.1701/4573.45791

Silvia Capuzzi, Federico Baldisseri, Antonella Cacchione, Andrea Carai, Francesco Fabozzi, Antonio Pietrabissa, Angela Mastronuzzi, Alberto Eugenio Tozzi, Diana Ferro

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引用次数: 0

摘要

本研究提出了一种基于人工智能的两阶段模型来预测儿科肿瘤患者的手术等待时间。该模型使用来自1478名患者和6145例手术的真实数据，首先对手术紧迫性进行分类，然后估计紧急病例的等待时间。在这两个阶段，随机森林算法都是表现最好的算法，SHAP分析发现了类似的关键预测特征。结果支持人工智能在改善手术计划、资源分配和临床决策方面的作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modello multi-step basato su intelligenza artificiale per il timing chirurgico in oncologia pediatrica.

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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来源期刊

Recenti progressi in medicina Medicine-Medicine (all)

CiteScore

0.90

自引率

0.00%

发文量

143

期刊介绍： Giunta ormai al sessantesimo anno, Recenti Progressi in Medicina continua a costituire un sicuro punto di riferimento ed uno strumento di lavoro fondamentale per l"ampliamento dell"orizzonte culturale del medico italiano. Recenti Progressi in Medicina è una rivista di medicina interna. Ciò significa il recupero di un"ottica globale e integrata, idonea ad evitare sia i particolarismi della informazione specialistica sia la frammentazione di quella generalista.