用联邦学习预测透析患者生存期，保护隐私同时提升模型效果。
Predicting Survival of Hemodialysis Patients using Federated Learning
- 通过联邦学习聚合多中心数据，避免直接共享敏感医疗信息。
- 在印度最大透析网络NephroPlus上验证，性能优于本地训练模型。
- 适合关注医疗隐私保护与个性化治疗的临床研究者使用。
接受血液透析的患者若被错误识别为肾移植候补名单成员，可能导致等待时间延迟。因此，准确预测其生存时间对优化等待名单和制定个性化治疗方案至关重要。然而，此类预测通常需要大量高质量但敏感的医疗数据，而这些数据分散在不同医疗机构中，单个机构的数据量小且多样性不足，导致本地训练的生存模型表现不佳。为此，本文提出在透析患者生存预测中应用联邦学习（Federated Learning, FL），可在不共享原始数据的前提下实现比本地模型更优的性能。尽管联邦学习应用日益广泛，但在生存分析及透析患者场景中的研究仍较稀少。本文基于印度最大的私人透析中心网络NephroPlus的数据，系统评估了联邦学习在该任务中的表现。
原文摘要 · Abstract (English)
Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival time is crucial for optimizing waiting lists and personalizing treatment plans. Predicting survival times for patients often requires large quantities of high quality but sensitive data. This data is siloed and since individual datasets are smaller and less diverse, locally trained survival models do not perform as well as centralized ones. Hence, we propose the use of Federated Learning in the context of predicting survival for hemodialysis patients. Federated Learning or FL can have comparatively better performances than local models while not sharing data between centers. However, despite the increased use of such technologies, the application of FL in survival and even more, dialysis patients remains sparse. This paper studies the performance of FL for data of hemodialysis patients from NephroPlus, the largest private network of dialysis centers in India.
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