Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates visual features from lung ultrasound images with clinical data to enhance the prediction of excess body fluid. Our framework employs independent encoders to extract features for each modality and combines them through a cross-domain attention mechanism to capture complementary information. By framing the prediction as a classification task, the model achieves significantly better performance than regression. The results demonstrate that multimodal models consistently outperform single-modality models, particularly when attention mechanisms prioritize tabular data. Pseudo-sample generation further contributes to mitigating the imbalanced classification problem, achieving the highest accuracy of 88.31%. This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes.
@article{arxiv.2409.08790,
title = {A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data},
author = {Tianqi Yang and Nantheera Anantrasirichai and Oktay Karakuş and Marco Allinovi and Alin Achim},
journal= {arXiv preprint arXiv:2409.08790},
year = {2024}
}
Comments
In the experiment, for the classification tasks, the network was informed with ground truth during training, significantly improving the performance. This makes the results invalid. Therefore, corrections and more validations are needed to evaluate the performance of the method