Introduction:
Accurate prediction of dry weight in hemodialysis patients is essential, as dry weight overestimation and underestimation are fraught with complications. Overestimation of dry weight results in chronic fluid overload resulting in hypertension, left ventricular hypertrophy, increased morbidity, and mortality. On the other hand,an underestimation of Dry weight can result in excessive fluid removal resulting in hypotension, cramps, nausea and vomiting. Traditional methods for estimating dry weight rely on clinical judgment and patient symptoms, which can be subjective and prone to errors. This paper explores the application of deep learning techniques to enhance the prediction of dry weight by leveraging clinical, dialysis, laboratory and investigation data.
Methods:
Key inputs include pre-dialysis weight, blood pressure, fluid intake/output, dialysis parameters, laboratory and investigation results. Five different models, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Random Forest Regressor, Support Vector Regressor, and Linear Regressor, are employed to capture both static and temporal patterns in patient data.
Results:
The performance of these models was compared using regression metrics such as Mean Squared Error (MSE) as the primary metric. The ANN model outperformed the other models, achieving the lowest MSE of 3.36, followed by the Random Forest Regressor (MSE = 13.66) and Support Vector Regressor (MSE = 22.7). The LSTM model and Linear Regressor exhibited poor performance. A weighted ensemble model combining the strengths of the ANN, LSTM, and SVR models further improved the prediction accuracy, achieving an MSE of 356. The models are being subjected to more data training which are expected to give better predictions. These networks offer several key advantages for predicting dry weight in nephrology, particularly in capturing temporal dependencies, handling Long-term dependencies, adaptability to dialysis session length, managing irregular data intervals, tracking the dynamic shifts in body fluids that occur over time during dialysis, Patient-Specific learning, handling noisy clinical data and adaptability to Long-Term monitoring in patient data.
Conclusions:
The study highlights the promising application of deep learning techniques, in predicting dry weight for hemodialysis patients, demonstrating their potential to improve prediction accuracy over traditional methods. By integrating deep learning models into clinical workflows, real-time dry weight predictions can aid healthcare providers in optimizing dialysis treatment and improving patient outcomes.
I have no potential conflict of interest to disclose.
I did not use generative AI and AI-assisted technologies in the writing process.