预测认知障碍患者的认知衰退缺血性中风与大脑出血的高风险:一种机器学习方法

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Eun Namgung¹, Young Sun Kim², Sun U Kwon³

¹Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea.

Frontiers in neurology

|August 11, 2025

概括

机器学习可以在9个月内准确预测中风患者的认知衰退. 这有助于识别高风险个体进行早期干预,并改善中风后认知障碍 (PSCI) 的结果.

科学领域:

神经学神经学
人工智能在医学中的应用
认知科学认知科学

背景情况:

在中风患者的快速认知衰退增加了中风复发的风险.
预测中风后认知障碍 (PSCI) 恶化有助于预防痴呆症.
机器学习模型可以预测PSCI患者9个月的认知衰退.

研究的目的:

预测PSCI急性缺血性中风患者的9个月认知衰退.
使用机器学习识别认知衰退的关键预测因素.
为了比较不同的机器学习算法对这个预测任务的有效性.

主要方法:

对109名患有PSCI的急性缺血性中风患者进行了回顾性研究.
利用了临床,成像数据和韩国-迷你心理状态检查 (K-MMSE) 评分.
训练并比较了CatBoost,AdaBoost,XGBoost和物流回归模型.

主要成果:

CatBoost获得了最高的预测性能 (AUC 0.897,准确率为 0.873).
关键预测因素包括基线K-MMSE,NIH中风量表和损伤体积比率.
认知衰退患者在药物治疗开始的间隔较长.

结论:

CatBoost有效地识别出患有认知衰退高风险的缺血性中风患者.
早期识别可以为更好的PSCI预后提供有针对性的干预措施.
了解风险和保护因素对于管理中风后的认知衰退至关重要.

关键词:

大脑出血脑出血认知能力下降认知能力下降缺血性中风中风机器学习是机器学习.在中风后的认知障碍.

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