用联邦学习保护隐私，实现个性化心血管风险预测
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario Exploration
- 通过联邦学习在不传输原始数据下训练模型
- 解决健康数据语义与格式不一致问题
- 适合关注隐私保护的医疗健康应用
心血管疾病（CVD）仍是导致死亡的主要原因，通过个性化干预进行一级预防至关重要。本文提出MyDigiTwin框架，将健康数字孪生与个人健康环境结合，使患者能在保护数据隐私的前提下探索个性化健康场景。该框架采用联邦学习，在分布式数据上训练预测模型而无需传输原始数据，并引入一种新型数据调和机制，解决健康数据中的语义与格式不一致问题。一个概念验证展示了利用队列数据调和并训练隐私保护型CVD预测模型的可行性。该框架为前瞻性、个性化的心血管护理提供了可扩展解决方案，并为未来在真实医疗环境中的应用奠定基础。
原文摘要 · Abstract (English)
Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework that integrates health digital twins with personal health environments to empower patients in exploring personalized health scenarios while ensuring data privacy. MyDigiTwin uses federated learning to train predictive models across distributed datasets without transferring raw data, and a novel data harmonization framework addresses semantic and format inconsistencies in health data. A proof-of-concept demonstrates the feasibility of harmonizing and using cohort data to train privacy-preserving CVD prediction models. This framework offers a scalable solution for proactive, personalized cardiovascular care and sets the stage for future applications in real-world healthcare settings.
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