一个新的机器学习模型,用于预测90天和365天的短期ASCVD风险
Tomer Gazit1, Hanan Mann1, Shiri Gaber1
1Hello Heart, Inc., Menlo Park, CA, United States.
Frontiers in digital health
|November 18, 2024
概括
使用电子健康记录和移动健康数据的新机器学习模型与传统工具相比,提供了优越的短期动脉样硬化心血管疾病风险预测. 这种方法增强了高血压患者的个性化预防.
科学领域:
- 心血管疾病研究研究.
- 机器学习在医疗保健中的应用
- 数字健康干预措施 数字健康干预措施
背景情况:
- 目前的动脉样硬化心血管疾病 (ASCVD) 风险工具提供长期预测,但可能无法有效地推动行为改变.
- 使用移动健康 (mHealth) 和电子健康记录 (EHR) 的短期风险预测可以改善临床决策和患者参与.
研究的目的:
- 利用mHealth和EHR数据,为高血压患者开发一个短期ASCVD风险预测模型.
- 将新模型的性能与现有的风险评估工具 (如聚合队列方程 (PCE) 和PREVENTTM) 进行比较.
主要方法:
- 一项追溯的队列研究,对51,127名高血压参与者 (年龄≥18) 进行了自我管理计划 (2015年1月至2024年1月).
- 开发一种机器学习 (ML) 模型,使用来自家用监控器的EHR数据和mHealth测量 (血压,心率).
- 将ML模型的性能与PCE和PREVENT分数进行比较.
主要成果:
- 使用291个特征的XgBoost ML模型在90天和365天的预测期内在区分ASCVD风险方面表现优于PCE和PREVENT.
- 对于90天的预测,C-统计是0.81 (XgBoost) 与0.74 (PCE) 和0.65 (PREVENT) 相比.
- 移动健康测量改善了365天的风险预测 (ROC-AUC为0.82比0.80没有移动健康).
结论:
- 与传统工具相比,基于EHR和mHealth的ML模型提供了优越的短期ASCVD预测.
- 这种方法促进了个性化的预防策略,特别是对于那些对现有工具的数据不完整的患者.
- 对该框架的进一步研究,包括额外的移动健康数据,可以提高预测能力和适用性.
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