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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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使用机器学习方法预测心血管风险. 性别特定的差异.

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概括

机器学习算法有效地使用现实数据预测主要心血管事件 (MACE). 药物坚持是个性化预防心血管风险评估的关键因素.

关键词:
在XGBoost中使用.对治疗的坚持.心血管疾病心血管疾病机器学习是机器学习.随机的森林随机的森林

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科学领域:

  • 心脏病学 心脏病学
  • 数据科学数据科学数据科学
  • 公共卫生 公共卫生

背景情况:

  • 机器学习 (ML) 对评估心血管风险因素 (CVRF) 和主要心血管事件 (MACE) 的传统评分系统具有优势.
  • 现实世界数据 (RWD) 的可用性使得临床实践的ML算法训练成为可能.
  • 机器学习模型可能会改善个性化的心血管风险预测.

研究的目的:

  • 用XGBoost和随机森林ML算法评估主要心血管事件 (MACE) 风险.
  • 将这些算法应用于真实世界数据 (RWD),按性别分层.
  • 为了比较XGBoost和Random Forest在预测MACE方面的表现.

主要方法:

  • 这项研究包括52393名受试者,从2018年到2020年进行了随访.
  • 对于每个ML算法 (XGBoost,随机森林) 生成了三种模型,包括年龄和血液检测,CVRF和药物坚持的组合.
  • 分析了心血管风险因素 (CVRF) 和药物坚持.

主要成果:

  • 总共有581次主要心血管事件 (MACE) 发生;发生率为女性1%和男性1.3%.
  • 高血压和高胆固醇是最常见的CVRFs. 治疗坚持程度各不相同,抗高血压药的坚持率最高,抗糖尿病药的坚持率最低.
  • 年龄是MACE风险的主要因素,其次是坚持服用抗糖尿病药物. 两种ML算法都显示出类似的性能.

结论:

  • 机器学习 (ML) 模型有效地使用现实世界数据 (RWD) 评估心血管风险.
  • 药物坚持是主要心血管事件 (MACE) 的重要预测因素.
  • ML应用程序支持初级保健中的个性化预防策略.