MyDigiTwin:一个保护隐私的框架,用于个性化的心血管风险预测和情景探索
Héctor Cadavid1, Hyunho Mo2, Bauke Arends3
1Netherlands eScience Center, Matrix THREE, Science Park 402, Amsterdam, 1098 XH, The Netherlands.
MyDigiTwin使个性化的心血管疾病预防使用健康数字双胞胎和联合学习. 这种保护隐私的框架训练了分布式数据模型,用于主动的健康管理.
科学领域:
- 数字健康数字健康
- 心血管疾病研究研究
- 医疗信息学 医疗信息学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 初级预防策略对于管理心血管疾病至关重要.
- 个性化干预需要安全访问个人健康数据.
研究的目的:
- 推出MyDigiTwin,这是一个整合健康数字双胞胎和个人健康环境的框架.
- 为了使患者能够探索个性化的健康场景,同时确保数据隐私.
- 开发用于心血管疾病预测的隐私保护模型.
主要方法:
- 利用联合学习在分布式数据集上训练预测模型,而不需要原始数据传输.
- 开发了一个新的数据协调框架,以解决语义和格式不一致的问题.
- 实施了使用队列数据进行心血管疾病预测模型培训的概念验证.
主要成果:
- 证明了协调各种健康数据的可行性.
- 使用联合学习成功训练了保护隐私的CVD预测模型.
- 在概念验证中验证了MyDigiTwin框架的功能.
结论:
- MyDigiTwin为主动和个性化的心血管保健提供了一个可扩展的解决方案.
- 该框架增强了患者在管理其健康方面的权力.
- 它为未来在心血管疾病预防方面的实际医疗保健应用奠定了基础.
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