在心力衰竭预后中评估抗高血压药物的药物有效性:利用电子健康记录的复合临床终点和生物标志物
Shaika Chowdhury1, Yongbin Chen2, Xiao Ma3
1Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN, USA.
概括
电子健康记录 (EHR) 可以预测心力衰竭患者的抗高血压药物的有效性. 这项研究使用了EHR数据和深度学习,在预测药物反应方面达到97%的准确性.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 药物基因组学 药物基因组学
背景情况:
- 动脉高血压是心力衰竭的主要危险因素.
- 目前的抗高血压药物选择面临的挑战是由于患者间反应的变化.
- 药物遗传学研究是昂贵的,并且很难在临床上实施.
研究的目的:
- 利用电子健康记录 (EHR) 来预测心力衰竭中抗高血压药物的有效性.
- 开发和评估针对个性化抗高血压治疗的深度学习模型.
主要方法:
- 利用来自约9500名心力衰竭患者的EHR数据的临床事件和生物标志物.
- 开发了注释策略,以从EHR序列中确定抗高血压疗效.
- 在注释式EHR数据上培训和评估监督深度学习分类器.
主要成果:
- 使用训练有素的深度学习分类器实现了0.97的F1性能.
- 证明了EHR数据在预测两种抗高血压药物类别药物反应方面的有效性.
结论:
- 电子健康记录是抗高血压药物的有效性研究中未得到充分利用的资源.
- 在EHR数据上训练的深度学习模型可以准确地预测患者对抗高血压药物的反应.
- 这种方法为药物遗传学研究提供了一个具有成本效益的替代方案,用于指导治疗.
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