机器学习模型的开发和验证,用于预测PCI后ACS患者的长期抑郁风险:回顾性队列研究
Huasheng Lv1,2, Fengyu Sun1,2, Yuchen Zhang1,2
1State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University, Urumqi, Xinjiang, People's Republic of China.
International journal of general medicine
|June 13, 2025
概括
机器学习模型预测经皮肤冠状动脉干预 (PCI) 后急性冠状动脉综合征 (ACS) 患者的长期抑郁风险. 关键预测因素包括NYHA分类和TSH,有助于早期风险分层和干预.
科学领域:
- 心脏病学 心脏病学
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 抑郁症是急性冠状动脉综合征 (ACS) 后皮肤冠状动脉干预 (PCI) 患者的一个重大问题.
- 早期识别患有长期抑郁症高风险的个体对于及时干预至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测PCI后ACS患者的长期抑郁风险.
- 为了确定PCI后抑郁症的关键预测因素.
主要方法:
- 1951年ACS患者接受PCI的回顾性队列研究.
- 使用Boruta算法进行特征选择;用受限立方线分析评估非线性关联.
- 训练并验证了六种ML模型;使用AUC,SHAP和决策曲线分析评估性能.
主要成果:
- 19.6%的患者患有长期抑郁症.
- 确定了十个关键预测因素,包括NYHA分类,糖尿病,TSH和LVEF.
- LGBM和XGBoost模型显示最高的歧视 (AUC高达0.849培训,0.699验证);SHAP分析强调NYHA,TSH和糖尿病具有影响力.
结论:
- 开发的ML模型显示了PCI后抑郁症早期风险分层的潜力.
- 结果可以为有针对性的临床干预提供信息,以改善患者的治疗结果.
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