基于机器学习的死亡率预测模型使用最小特征来协助终身护理决策
Tae Hoon Kong1, Jae Ha Kim2, Mi Sun Kim3
1Department of Otorhinolaryngology - Head and Neck Surgery, Yonsei University Wonju College of Medicine, Wonju, KOR.
Cureus
|December 4, 2025
概括
机器学习准确地预测了息性放射治疗 (PRT) 患者的30天死亡率. 使用关键因素的简化模型为个性化终身护理规划提供了一个实用的工具.
科学领域:
- 在瘤学瘤学.
- 辐射疗法 辐射疗法
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 国际指导方针建议用于息性放射治疗 (PRT) 的单次或低分离.
- 准确预测预期寿命是具有挑战性的,阻碍了个性化的终身护理.
- 根据个体患者的预期寿命量身定制PRT需要强大的预测工具.
研究的目的:
- 开发基于机器学习 (ML) 的模型,用于预测接受PRT的患者的30天死亡率.
- 确定预测短期死亡率的关键临床变量.
- 创建一个实用的决策支持工具,为个性化的终身护理.
主要方法:
- 对接受PRT治疗晚期癌症的318名患者的回顾性分析.
- 使用22个变量开发和评估ML模型 (额外的树木,随机森林,LightGBM,XGBoost).
- 使用准确性,精度,回忆,特异性和F1分数进行绩效评估.
主要成果:
- 轻GBM模型表现出最好的性能 (精度:0.725,F1得分:0.720).
- 预测<30天死亡率的预测因素包括不良的ECOG状态,低专蛋白,高中性粒细胞与淋巴细胞的比率和低淋巴细胞数量.
- 最小变量模型 (MVM) 实现了与全变量模型 (FVM) 相比的性能,但复杂性降低.
结论:
- 一个基于ML的新型模型可以预测PRT患者的死亡率,帮助评估预期寿命.
- 该模型确定了反映患者状况和瘤负担的显著预测因素.
- MVM提供了一个潜在的可解释和实用的工具,用于临床决策支持终身护理.
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