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开发,外部验证和部署RFAN-ML:一种机器学习模型,用于估计脏切除术后的功能

Jesse Persily¹, Steven L Chang², Chen Chen¹

¹Department of Urology, NYU Grossman School of Medicine, New York, NY.

JCO clinical cancer informatics

|November 7, 2025

概括

我们开发了一种机器学习 (ML) 模型,RFAN-ML,用于预测切除术后的功能. 这个工具有助于个性化患者护理和脏瘤患者的手术规划.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
在瘤学瘤学.
数据科学数据科学数据科学

背景情况:

部分切除术是小瘤的首选,但具有外科手术期间的风险.
精确估计脏切除术后的功能对于患者咨询和外科决定至关重要.
现有的预测模型往往缺乏外部验证和用户友好的界面.

研究的目的:

开发和外部验证一个机器学习 (ML) 模型,RFAN-ML,用于估计腎切除术后的长期功能.
为预测脏切除术后功能提供一个用户友好的工具.

主要方法:

利用来自两个学术医学机构的数据.
雇佣Boruta功能选择以确定关键预测因素:年龄,BMI,手术前功能和切除术类型.
训练和评估了六个ML回归模型,选择了表现最好的模型为RFAN-ML.

主要成果:

与现有基准相比,RFAN-ML表现优越或具有竞争力的表现.
实现了根平均二次误差 (RMSE) 的16.6 (95% CI,15.6至17.5).
使用R平方和平均绝对误差指标来评估性能.

结论:

一个经过验证的ML模型RFAN-ML准确地预测了脏切除术后的功能.
该模型可以在线获得,促进个性化的患者咨询和手术规划.
RFAN-ML有可能改善瘤患者的护理和治疗结果.

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