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开发和验证深度学习模型,用于预测症状内动脉狭窄症患者的长期预后

Qianjin Ding¹, Shaojie Zhang², Li Pan²

¹Department of Neurosurgery, Xinxiang Center Hospital, Xinxiang, Henan, 453000, People's Republic of China.

International journal of general medicine

|October 30, 2025

概括

与逻辑回归相比,深度学习模型显著提高了对内动脉狭窄症 (ICAS) 进展的预测. 这一进步有助于在中风患者的早期风险分层,使得及时干预和个性化治疗策略.

科学领域:

神经学神经学
医疗信息学医疗信息学
心血管研究研究心血管研究

背景情况:

症状性内动脉狭窄症 (ICAS) 是缺血性中风的主要原因.
ICAS进展增加了中风复发风险,并使患者的结果恶化.
准确预测ICAS进展对于有效的患者管理至关重要.

研究的目的:

开发和验证后勤回归和深度学习模型,用于预测症状ICAS进展.
为了比较这些模型的预测性能.

主要方法:

一项追溯研究对266名症状ICAS患者进行了超过3年的随访.
数据预处理包括规范化,特征选择和类平衡.
后勤回归和深度学习模型进行了训练和验证.

主要成果:

深度学习模型实现了更高的曲线下的面积 (AUC) 0.863 (验证) 与物流回归的0.767相比.
深度学习模型显示了统计学上显著的改善 (p=0.016).
确定了关键预测因素:高血压,糖尿病,狭窄程度,以及之前的中风史.

结论:

与物流回归相比,深度学习模型为ICAS进展提供了更高的预测准确性.
这些模型在风险分层方面具有显著的临床实用性潜力.
早期识别高风险患者可以指导个性化治疗策略.

关键词:

深度学习是一种深度学习.逻辑回归的逻辑回归方法患病进展的风险.有症状的内动脉狭窄.

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