Predicting Survival of Hemodialysis Patients using Federated Learning: A Nation-wide Study
Raju, Abhiram Vepakomma, Praneeth
Raju, Abhiram
Vepakomma, Praneeth
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Supervisor
Department
Computer Vision
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Type
Conference proceeding
Date
2025
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Language
English
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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 don’t perform as well as centralized ones. Hence, we propose the use of Federated Learning (FL) in the context of predicting survival for hemodialysis patients. 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, India’s largest network of dialysis centers.
Citation
A. Raju and P. Vepakomma, "Predicting Survival of Hemodialysis Patients using Federated Learning: A Nation-wide Study," 2024 IEEE MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, USA, 2024, pp. 1-5, doi: 10.1109/URTC65039.2024.10937595.
Source
2024 IEEE MIT Undergraduate Research Technology Conference (URTC)
Conference
2024 IEEE MIT Undergraduate Research Technology Conference (URTC)
Keywords
Federated Learning, Hemodialysis, Survival Analysis, Machine Learning
Subjects
Source
2024 IEEE MIT Undergraduate Research Technology Conference (URTC)
Publisher
IEEE