用图形神经网络和变压器对心力衰竭患者进行分层,使用电子健康记录来优化药物反应预测
Shaika Chowdhury1, Yongbin Chen2, Pengyang Li3
1Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN 55902, United States.
概括
这项研究开发了一个基于图形的AI模型,使用电子健康记录来预测心力衰竭治疗反应. 该模型确定了患者子组,改善了个性化医疗和药物疗效预测.
科学领域:
- 计算生物学是一种计算生物学.
- 医学中的人工智能.
- 心血管研究的心血管研究.
背景情况:
- 心力衰竭 (HF) 影响全球数以百万计的人,患者对治疗的反应在患者之间有很大差异.
- 基于人口数据的当前HF治疗方法,由于缺乏个性化,通常会产生低于最佳的结果.
- 预测个体患者对HF药物的反应仍然是临床实践中的一个关键挑战.
研究的目的:
- 开发针对患者的特定计算模型,用于预测心力衰竭治疗结果.
- 通过识别可能从现有药物中受益的特定HF患者亚组,提高药物反应预测.
- 利用一个庞大的电子健康记录 (EHR) 数据库,为个性化的HF治疗策略提供帮助.
主要方法:
- 开发了一个新的基于图形的模型,集成图形神经网络和变压器架构.
- 将患者的电子病历数据转化为图形结构进行分析.
- 利用K-Means集群在患者图表表示上,以定义子组并改善药物反应预测.
主要成果:
- 基于图形的模型在预测使用NT-proBNP在11,627名高压患者中的药物反应方面显著优于传统模型.
- 在五种药物类别中达到0.0043的最佳根平均平方误差 (RMSE).
- 确定了四个不同的患者亚组,具有不同的特征和结果,表现出优越的预测能力 (最佳RMSE平均值为0.0032) 与现有的HF亚型相比.
结论:
- 基于电子心脏病记录数据的图形建模增强了心力衰竭治疗策略.
- 使用这种AI方法的患者分层识别了从量身定制的反应预测中受益的细分.
- 纵向EHR数据与基于图形的AI相结合,具有在高频中进行个性化预后预测的巨大潜力.
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