机器学习和名图是预测食道癌患者手术后营养不良风险的准确工具
Zhenmeng Lin1,2, Hao He1, Mingfang Yan2
1Department of Thoracic Oncology Surgery, Clinical Oncology School of Fujian Medical University & Fujian Cancer Hospital, Fuzhou, China.
Frontiers in nutrition
|July 3, 2025
概括
食道癌手术后的术后营养不良是常见的. 机器学习和名图有效预测这种风险,帮助患者护理.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 外科手术的结果
背景情况:
- 手术后营养不良是食道癌手术后的常见并发症.
- 它显著影响患者的康复和长期预后.
研究的目的:
- 开发和验证术后营养不良风险的预测模型.
- 为了利用机器学习算法和一个nomogram风险估计后食道切除术.
主要方法:
- 对1693名接受食道癌治愈手术的患者进行分析.
- 使用最小绝对收缩和选择运算符 (LASSO) 算法进行特征选择.
- 构建和评估八个机器学习模型和一个名ogram.
主要成果:
- 手术后营养不良的发病率在开发和验证队列中分别为45.4%和50.7%.
- 随机森林 (RF) 模型实现了最佳性能 (AUC 0.820/0.805).
- 使用五个预测因素 (性别,年龄,BMI,新辅助疗法,肉症) 的名图显示了可比的歧视 (AUC 0.801/0.795).
结论:
- 机器学习和名图是预测营养不良风险的准确工具.
- 诺米图为个性化风险分层提供了卓越的临床解释性.
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