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调整风险预测模型以适应当地居民
Aniket N Zinzuwadia1, Olga Mineeva2, Chunying Li1
1Brigham & Women's Hospital, Boston, Massachusetts.
JAMA cardiology
|September 18, 2024
概括
这项研究开发了一种可解释的机器学习模型 (MLM-PREVENT),以改善心血管风险预测. 该模型提高了当地人口中AHA-PREVENT方程的准确性,同时保持了临床解释性.
科学领域:
- 心血管医学 心血管医学
- 医疗保健中的机器学习
- 医疗信息学 医疗信息学
背景情况:
- 心血管风险估计对于患者护理至关重要.
- 在指南中推的风险模型在应用于当地人口时可能存在局限性.
- 当地重新校准可以解决这些局限性.
研究的目的:
- 开发一种机器学习 (ML) 方法来增强美国心脏协会对当地人口的预测心血管疾病事件风险 (AHA-PREVENT) 方程.
- 为了保持临床解释性,同时增加模型性能.
- 为电子健康记录系统提供可实施的工具.
主要方法:
- 一项队列研究利用了2007年至2016年的新英格兰电子健康记录队列 (95,326名患者).
- 通过使用极端梯度增强 (XGBoost) ML模型 (MLM-PREVENT) 调整了AHA-PREVENT模型.
- 多媒体营销被单调地限制在保留已知的风险因素关联;歧视,校准和重新分类被评估.
主要成果:
- 与AHA-PREVENT模型相比,MLM-PREVENT在各种风险类别和性别子组中显示出更好的校准.
- 在亚洲,黑人和白人个体中,校准保持或改进.
- 在MLM-PREVENT和AHA-PREVENT之间,歧视是可比的;MLM-PREVENT将11.5%的患者重新分类为7.5%的风险值.
结论:
- 一种可解释的ML方法提高了AHA-PREVENT在当地人口中的准确性,同时保留了原来的风险关联.
- 这种方法可以重新校准其他风险工具,并且适合EHR集成.
- 这些发现支持通过局部化,可解释的ML模型改进心血管风险评估.
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