可解释的机器学习算法可预测腹膜透析患者的心血管事件
Qiqi Yan1,2, Guiling Liu1,2, Ruifeng Wang1,2
1Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
BMC medical informatics and decision making
|April 23, 2025
概括
随机生存森林 (RSF) 模型在预测腹腔透析 (PD) 患者心血管事件 (CVE) 方面表现优异. 这种机器学习方法有助于识别高风险个体,以便更好地管理.
科学领域:
- 腎病學和心血管醫學.
- 生物统计学和机器学习
背景情况:
- 心血管事件 (CVE) 是接受腹腔透析 (PD) 的患者发病和死亡的主要原因.
- 准确预测CVE风险对于及时干预和改善患者结果至关重要.
研究的目的:
- 将机器学习算法 (极端梯度提升和随机生存森林) 的预测性能与PD患者心血管事件的Cox比例危险回归进行比较.
- 在这个患者群体中确定CVE的关键预测因素.
主要方法:
- 一组318名接受PD导管治疗的患者被追溯分析.
- 患者被随机分配到训练 (70%) 和验证 (30%) 组.
- 考克斯回归,XGBoost和RSF模型使用依赖时间的曲线下的面积 (AUC) 和一致性指数 (C-index) 进行了开发和验证.
主要成果:
- 随机生存森林 (RSF) 模型在验证组中表现出优异的预测性能,C指数为0.725和有利的时间依赖AUC值 (1年:0.812,3年:0.836,5年:0.706).
- 确定的主要预测因素包括血小板计数,年龄,4小时透析剂与肌素比率 (4hD/Pcr),左心脏直径和左心室直径.
- 通过RSF模型分层的高风险和低风险组之间观察到累积CVE无生存的显著差异.
结论:
- 随机生存森林 (RSF) 模型提供了一种强大且潜在的优越方法,用于评估腹腔透析患者心血管事件风险.
- 这种机器学习方法可以帮助临床医生分层患者并实施个性化的预防策略.
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