与CADASIL治疗患者中风无存活预测模型的比较
Henri Chhoa1, Hugues Chabriat2,3, Sylvie Chevret1
1ECSTRRA Team, Université Paris Cité, UMR1153, INSERM, Paris, France.
Scientific reports
|December 17, 2023
概括
预测大脑自体主导性动脉病变与皮下心脏病发作和白细胞脑病变 (CADASIL) 的无中风存活率至关重要. 机器学习模型,特别是梯度增强和随机生存森林与LASSO特征选择,显示强大的预测性能.
科学领域:
- 神经学 神经学
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
背景情况:
- 大脑自体主导性动脉病变与皮下心脏病发作和白细胞脑病变 (CADASIL) 是由NOTCH3基因突变引起的遗传疾病.
- 卡达西尔表现出异质的进展,影响临床评分,并导致各种临床事件.
- 准确预测疾病进展,特别是无中风生存时间,对于患者管理至关重要.
研究的目的:
- 为了比较Cox比例危险回归的预测性能与CADASIL中无中风生存的机器学习模型.
- 为了评估四种不同的特征选择方法与这些模型一起的有效性.
- 确定用于评估CADASIL疾病进展的最佳建模策略.
主要方法:
- 利用来自482名CADASIL患者队列的人口统计,临床和磁共振成像数据.
- 采用Cox比例危险回归和机器学习模型 (梯度增强,随机生存森林).
- 应用了一个嵌套的交叉验证程序与LASSO特征选择,评估性能使用时间依赖的障碍得分和AUC在5年.
主要成果:
- 使用LASSO组件式梯度增强模型实现了最佳整体性能 (平均布赖尔得分:0.165).
- 使用LASSO随机生存森林模型显示了最高的区分能力 (平均AUC:0.773).
- 这两种模型,当与LASSO特征选择相结合时,都超过了传统的回归方法.
结论:
- 机器学习模型,特别是梯度增强和随机生存森林,结合LASSO特征选择,在CADASIL中有效预测无中风生存率.
- 与传统方法相比,这些先进的建模技术提供了更好的准确性和区别.
- 这些发现支持使用这些预测工具来更好地评估和管理CADASIL患者.
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