研究人员开发了一种联邦深度学习方法，可在保护患者数据隐私的同时提高心血管疾病风险预测的准确性。该方法整合了Lifelines和Rotterdam Study两个不同的队列，实现了在不直接共享数据的情况下进行协作模型训练。与本地训练的模型相比，联邦模型展示了更强的预测性能，两个队列的C统计量均有显著提高。 AI
影响 增强了医疗保健领域中保护隐私的AI应用，有可能提高跨机构的诊断准确性。
排序理由 该集群包含一篇详细介绍新研究方法的学术论文。
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arXiv:2607.08595v1 Announce Type: new Abstract: Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing p…
Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables co…