测试一种新的设计框架,用于面向患者的基于机器学习的预测,对心力衰竭减偿
Meghan Reading Turchioe1, So Hyeon Bang1, Afra Shamnath1
1Columbia University School of Nursing, New York, New York, USA.
JACC. Advances
|October 24, 2025
概括
一个智能手机应用程序原型有效地向心脏植入式电子设备 (CIED) 患者提供了心脏失补偿风险预测. 患者表现出高度的理解和适当的风险感知,表明应用程序.
科学领域:
- 生物医学信息学是生物医学信息学.
- 数字健康数字健康
- 面向患者的技术面向患者的技术.
背景情况:
- 患者对个人健康数据的访问越来越多,需要安全的方法来呈现机器学习预测.
- 心脏植入式电子设备 (CIED) 产生适合预测算法的数据.
研究的目的:
- 设计和评估一个面向患者的智能手机应用程序原型,用于显示心脏衰竭风险预测.
- 评估患者对算法输出的理解,风险感知和行为意图.
主要方法:
- 在智能手机应用程序原型中开发了算法输出呈现的设计框架.
- 在接受CIED的成年患者中进行了混合方法可用性评估.
- 评估患者客观理解的初级终点和风险感知和行为意图的二级终点.
主要成果:
- 在不同的风险变化条件下,观察到患者对算法输出的高理解率 (80-85%).
- 对算法值和贡献CIED传感器的理解程度各不相同 (60-93%).
- 显著的风险变化引起了70%的参与者的担忧,80%的参与者打算与他们的医生联系.
结论:
- 原型证明了患者的高度理解,适当的风险感知和预期的行动.
- 这些发现表明面向患者的应用程序的可行性,可以从预测算法提供可操作的健康见解.
- 这种方法可以使CIED患者能够更好地管理他们的心脏健康.
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