标题： 去中心化、协作性和隐私保护的多医院数据机器学习 显示英文标题

标题： Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data

摘要： 机器学习（ML）在医疗数据分析中展示了其巨大的潜力。 从不同来源和环境中收集的大数据集对于医疗保健中的ML模型来说是实现更好准确性和泛化能力的关键。 由于复杂的隐私和监管要求各不相同，跨不同医疗保健机构共享数据具有挑战性。 因此，允许多方在不直接共享这些数据或通过协作损害数据集隐私的情况下，利用每个方的私有数据集协同训练ML模型，这虽然困难但至关重要。 在本文中，我们通过提出去中心化、协作且隐私保护的多医院数据机器学习（DeCaPH）来解决这一挑战。 它提供了以下关键优势：（1）它允许不同方在不转移其私有数据集的情况下协同训练一个ML模型；（2）它通过限制在训练过程中各方之间共享内容可能引发的隐私泄露来保护患者隐私；（3）它在不依赖集中式服务器的情况下促进ML模型的训练。 我们在三个不同的任务上使用现实世界分布式的医疗数据集展示了DeCaPH的泛化能力和强大功能：使用电子健康记录进行患者死亡率预测，使用单细胞人类基因组进行细胞类型分类，以及使用胸部放射图像进行病理识别。 我们证明了使用DeCaPH框架训练的ML模型在效用-隐私权衡方面有所改进，表明它使模型在保持训练数据点隐私的同时具有良好的性能。 此外，总体而言，使用DeCaPH框架训练的ML模型优于仅使用来自各个独立方的私有数据集训练的模型，这表明DeCaPH增强了模型的泛化能力。 显示英文摘要

摘要： Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). It offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets; (2) it safeguards patient privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized server. We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. We demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing it enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability.