使用机器学习预测急性心肌梗塞和缺血性心脏病在初级保健心血管患者
N Salet1, A Gökdemir1,2, J Preijde2
1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
PloS one
|July 18, 2024
概括
在初级保健中,机器学习模型在预测急性心肌梗塞和缺血性心脏病方面明显优于SMART算法. 这些先进的ML工具为改善心血管疾病预测和个性化患者护理提供了潜力.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 心血管疾病 (CVD) 需要早期检测,最好是在初级保健机构.
- 预测急性心肌梗塞 (AMI) 和缺血性心脏病 (IHD) 对于有效的心血管疾病管理至关重要.
- 机器学习 (ML) 提供了一种新的方法来提高初级保健中CVD风险预测.
研究的目的:
- 开发和评估ML模型,用于预测初级保健患者的AMI和IHD.
- 将ML模型的性能与已建立的SMART风险预测算法进行比较.
- 确定有助于ML模型对心血管疾病风险准确性的关键预测因素.
主要方法:
- 利用了来自90家GP实践 (2011-2021) 的患者级医疗记录数据 (n=13,218).
- 构建了两个随机森林ML模型 (AMI和IHD) 和一个线性SMART算法模型.
- 采用时间交叉验证进行绩效评估,并确定了关键预测特征.
主要成果:
- 在所有指标上,ML模型在SMART算法上表现出优越的性能.
- 该AMI预测模型的准确度为0.97,AUC为0.96,布赖尔得分为0.03.
- 关键预测因素包括抗凝剂/抗血小板药物的使用,缩血压,平均血糖和eGFR.
结论:
- ML具有显著的潜力,可以提高初级保健中心血管疾病预测的准确性.
- ML模型可以支持个性化的风险评估,帮助初级保健医生制定预防策略.
- 虽然有效,但ML模型预测者的解释性需要进一步考虑临床整合.
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