An improved domain-adversarial network for predicting hemodialysis adequacy

Hemodialysis (HD) is the primary life-sustaining treatment for patients with end-stage renal disease (ESRD). However, current real-time monitoring methods during dialysis are costly, complex, and not widely adopted. Therefore, this study aims to propose a novel Kt/V prediction method that is accurate, low-cost, and compatible with various dialysis devices, in order to enhance the intelligence and efficiency of clinical hemodialysis treatment. We propose an improved Domain Adversarial Neural Network (DANN) framework for Kt/V prediction. The feature extractor integrates a Long Short-Term Memory (LSTM) network with a Kolmogorov-Arnold Network (KAN), where the B-spline activation functions in KAN enhance nonlinear modeling capabilities. Additionally, a multi-head attention mechanism is incorporated into the label predictor to capture long-range temporal dependencies. This architecture effectively mitigates data distribution shifts caused by inter-patient and inter-device variability. Experiments conducted on clinical and simulated data from 17 ESRD patients demonstrate that the proposed model achieves high prediction accuracy and significantly outperforms conventional deep learning models, including RNN, LSTM, and the original DANN. The proposed model enables precise, non-invasive estimation of dialysis adequacy (Kt/V), showing excellent generalization and clinical application potential. It provides a practical solution for real-time adequacy assessment, especially suitable for low- and mid-range dialysis devices.