机器学习用于早期发现孕产妇心血管疾病:使用电子健康记录数据的回顾性研究
Nawar Shara1,2, Roxanne Mirabal-Beltran3, Bethany Talmadge4
1MedStar Health Research Institute, Hyattesville, MD, United States.
JMIR cardio
|April 22, 2024
概括
机器学习 (ML) 在检测怀孕患者的心血管风险方面表现有前途,更早地识别高风险个体. 这项技术可以改善决策,减少母亲护理方面的差异.
科学领域:
- 心脏病学 心脏病学
- 产科 产科 产科 产科 产科
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病是美国孕产妇死亡的主要原因.
- 美国的孕产妇死亡率很高,不成比例地影响少数群体.
- 在怀孕期间早期发现心血管风险至关重要.
研究的目的:
- 为了评估所有怀孕经验的健康结果-心血管风险评估技术 (HOPE-CAT) ML算法.
- 评估HOPE-CAT在检测孕产妇心血管疾病方面的能力和时间.
主要方法:
- 使用HOPE-CAT ML算法对被删除的EHR数据进行回顾性分析.
- 对专家选择的风险因素和当前标准进行训练的算法.
- 生成并与临床结果和诊断进行比较的风险概况.
主要成果:
- 在诊断或干预之前,HOPE-CAT平均发现了56.8天的风险因素.
- 在早期检测心肌梗塞中表现最强.
- 孕前是发现的最常见的疾病.
结论:
- ML增强了产科中心血管疾病的早期检测.
- ML 合成患者数据以支持提供者决策.
- 这种方法可能有助于减少孕产妇健康差异.
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