Federated Competing Risk Analysis
Federated Competing Risk Analysis
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DOI：
10.1145/3583780.3614880
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Md Mahmudur Rahman;Sanjay Purushotham
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文献类型：
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作者：
Md Mahmudur Rahman;Sanjay Purushotham
对分布式医疗数据进行生存分析是一个重要的研究问题，因为隐私法和新兴的数据共享法规禁止跨多个机构共享敏感的患者数据。分布式医疗生存数据通常表现出异质性、非统一审查，并且涉及具有多种健康状况(竞争风险)的患者，这可能导致有偏见和不可靠的风险预测。为了应对这些挑战，我们建议使用联合学习(FL)进行具有竞争风险的生存分析。在这项工作中，我们提出了两个主要贡献。首先，我们提出了一种简单的估计一致性联合伪值(FPV)的算法，用于具有竞争风险和截尾的生存分析。其次，我们引入了一种新颖而灵活的基于FPV的深度学习框架Fedora，在不访问客户数据的情况下，在Fedora框架内联合训练我们提出的基于变压器的模型TransPseudo，该模型专门针对参与机构(客户)，从而保护了数据隐私。我们在以非IID和非一致审查属性为特征的真实世界分布式医疗数据集以及具有各种审查设置的合成数据上进行了广泛的实验。我们的结果表明，我们的Fedora框架与使用最先进的生存模型进行竞争风险分析的联邦学习框架相比，性能更好。
Conducting survival analysis on distributed healthcare data is an important research problem, as privacy laws and emerging data-sharing regulations prohibit the sharing of sensitive patient data across multiple institutions. The distributed healthcare survival data often exhibit heterogeneity, non-uniform censoring and involve patients with multiple health conditions (competing risks), which can result in biased and unreliable risk predictions. To address these challenges, we propose employing federated learning (FL) for survival analysis with competing risks. In this work, we present two main contributions. Firstly, we propose a simple algorithm for estimating consistent federated pseudo values (FPV) for survival analysis with competing risks and censoring. Secondly, we introduce a novel and flexible FPV-based deep learning framework named Fedora, which jointly trains our proposed transformer-based model, TransPseudo, specific to the participating institutions (clients) within the Fedora framework without accessing clients' data, thus, preserving data privacy. We conducted extensive experiments on both real-world distributed healthcare datasets characterized by non-IID and non-uniform censoring properties, as well as synthetic data with various censoring settings. Our results demonstrate that our Fedora framework with the TransPseudo model performs better than the federated learning frameworks employing state-of-the-art survival models for competing risk analysis.