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Updated: May 23, 2025

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使用统计学学习和可解释的人工智能预测性别特异性心血管疾病风险:HUNT研究
Virginia De Martin Topranin1, Atle Wiig-Fisketjøn2, Emma Botten2
1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
European journal of preventive cardiology
|March 10, 2025
概括
使用统计学学习的新型心血管疾病 (CVD) 风险模型显示,男性的预测能力有所改善. 性别特定的干预门可以增强男性和女性的心血管疾病预防策略.
科学领域:
- 心脏病学 心脏病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 目前的心血管疾病 (CVD) 风险预测模型,如NORRISK 2,仅解释了心血管疾病发病率的一小部分.
- 现有的模型可能无法充分捕捉性别特异性风险因素,需要改进的预测工具.
研究的目的:
- 开发改进的性别特异模型来预测10年心血管疾病风险.
- 建立针对心血管疾病干预的性别和年龄特定的门.
主要方法:
- 对31,946名没有先前心血管疾病的参与者 (40-79岁) 的数据进行分析.
- 使用极端梯度提升 (XGBoost) 和后勤回归的预测模型的开发,每个性别都有单独的模型.
- 使用接收器操作特征 (ROC) 和精度召回 (PR) 曲线的模型评估.
主要成果:
- 与NORRISK 2和SCORE2相比,XGBoost模型显著改善了男性的心血管疾病风险预测,由ROC-AUC和PR-AUC的增加表明.
- 对于女性来说,XGBoost和后勤回归模型都没有显示出与现有模型相比的显著改善.
- 对特定年龄和性别值的探索表明,与NORRISK 2值相比,灵敏度有所提高.
结论:
- 统计学学习和性别特异性风险因素为男性提供了更好的心血管疾病风险预测模型.
- 实施特定于性别的干预门可能会加强对男女的心血管疾病预防工作.
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