Optimization of fluid volume control in hemodialysis using federated learning

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Overhydration (OH) represents a significant challenge for hemodialysis patients, significantly affecting the outcomes of their treatment. Accurate prediction and management of overhydration are key to optimizing therapy and improving patients' quality of life. The aim of this paper is to present a federated learning (FL)-based approach designed to predict overhydration in hemodialysis patients, using a dataset comprising different clinical and bioimpedance parameters. Federated learning enables collaborative learning from multiple data sources while preserving the privacy and security of individual patient data. Research results show that federated learning has the potential as an effective tool for predictive modeling in clinical settings. The developed models achieve high performance in overhydration estimation, with metrics confirming their accuracy and reliability. The proposed approach achieved a R² of 0.9999999, a MAE of 0.00018 and an MSE of 0.0031, demonstrating its predictive strength and practical applicability. This study highlights the advantages of federated learning in using distributed data to advance predictive capabilities in healthcare. By overcoming challenges related to privacy and data security, the approach presented in this paper opens up opportunities for more personalized and accurate prognoses, potentially improving decision-making and patient care in hemodialysis.