Predicting Survival of Hemodialysis Patients using Federated Learning
This addresses the challenge of improving survival predictions for hemodialysis patients while protecting privacy, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of predicting survival times for hemodialysis patients to optimize transplant waiting lists, using federated learning on data from NephroPlus in India, and found it performed better than local models without sharing sensitive data.
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.