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可解释的机器学习用于预测急性缺血性中风的临床结果

Joonwon Lee¹, Kang Min Park¹, Seongho Park¹

¹Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.

Frontiers in neurology

|September 25, 2023

概括

机器学习准确地预测了急性缺血性中风患者的结果. 极端渐变增强模型确定了最初的NIHSS和早期的神经系统恶化作为关键预测因素,提供了有价值的临床见解.

科学领域:

神经学神经学
医疗信息学医疗信息学
生物统计学生物统计学

背景情况:

对急性缺血性中风 (AIS) 的准确预后对于有效的治疗计划至关重要.
机器学习 (ML) 提供了在AIS中预测患者结果的潜力.

研究的目的:

开发和验证一种ML模型,用于预测AIS患者的3个月功能结果.
使用可解释的人工智能方法识别和解释功能结果的关键预测因素.

主要方法:

使用多中心中风注册数据集进行模型开发和外部验证.
采用机器学习,包括极端梯度提升,以及SHapley添加式扩展 (SHAP) 来解释特征.
将3个月的功能结果分类为有利的 (mRS ≤2) 或不利的 (mRS ≥3).

主要成果:

极端梯度增强模型实现了强大的预测性能,AUC-ROC为0.790 (内部) 和高达0.873 (外部验证).
确定的关键预测因素包括最初的美国国立卫生研究院中风量表 (NIHSS),早期神经系统恶化 (END),年龄和白细胞计数.
SHAP分析提供了关于特征贡献和相互作用的见解,特别是在 END.

结论:

ML模型擅长预测AIS患者的3个月功能结果.
像SHAP这样的可解释的人工智能方法可以提高对模型预测和特征重要性的理解,帮助临床决策.

关键词:

大脑梗塞脑梗塞可解释的人工智能功能性结果的结果.机器学习是机器学习.修改后的排名级别表.预测预测预测预测一次性中风中风中风中风中风

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