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English(EN) Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

联邦深度学习提升心血管风险预测隐私性

作者 PulseAugur 编辑部 · [2 个来源] ·

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。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

联邦深度学习提升心血管风险预测隐私性

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hyunho Mo, Djura Smits, Mahlet A. Birhanu, Maarten J. G. Leening, Daniel Bos, Pim van der Harst, Esther E. Bron ·

    Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

    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…

  2. arXiv cs.LG TIER_1 English(EN) · Esther E. Bron ·

    Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

    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…

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