通过使用电子健康记录和心声回声学,对心力衰竭的表型与保存的喷射小组进行表型化
Morgane Pierre-Jean1, Benjamin Marut1, Elizabeth Curtis1
1CHU Rennes, Inserm, University of Rennes, LTSI-UMR 1099, hopital Pontchaillou, rue Henri Le Guillou, 35000 Rennes, France.
European heart journal open
|January 10, 2024
概括
机器学习使用临床和心声学数据识别了四种不同的心力衰竭与保留喷射率 (HFpEF) 现型. 这些表型显示出不同的预后,有助于个性化风险评估和HFpEF患者的治疗策略.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 保持喷射分数 (HFpEF) 的心力衰竭是一种异质的疾病.
- 低于最佳的管理可能是由于缺乏明显的HFpEF表型.
- 识别HFpEF表型对于个性化医学至关重要.
研究的目的:
- 使用机器学习识别不同的HFpEF表型.
- 为了利用电子健康记录和心声回声学进行表型化.
- 确定已识别的表型是否与不同的预后相关.
主要方法:
- 使用了大学医院的医疗信息数据库.
- 将机器学习算法应用于临床变量和心声回声数据.
- 将患者分为不同的表型组.
主要成果:
- 确定了HFpEF患者的四个不同的表型集群.
- 这些表型是基于结合的临床和心声学特征.
- 鉴定出的表型表现出与死亡率和心血管住院的预后显著不同.
结论:
- 人工智能可以有效地识别不同的HFpEF表型.
- 人工智能衍生的表型可以作为HFpEF风险分层的工具.
- 以表型为指导的治疗可能会改善HFpEF患者的结果.
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