Predicting Survival of Hemodialysis Patients using Federated Learning

TL;DR

This paper explores the application of federated learning to predict survival times of hemodialysis patients, aiming to improve model performance without sharing sensitive data across centers.

Contribution

It demonstrates the feasibility and effectiveness of federated learning for survival prediction in dialysis patients, a novel application in this medical context.

Findings

Federated learning outperforms local models in survival prediction.

Application of FL in dialysis patient data is feasible and effective.

Study conducted on data from India's largest dialysis network.

Abstract

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.