Ventilator Pressure Prediction Using Recurrent Neural Network: An Integrated Multimodal Deep Learning Framework for Adaptive and Real-Time Healthcare Applications

Abstract

Ventilator pressure prediction plays a crucial role in modern intensive care units, where accurate and adaptive control of respiratory support directly influences patient outcomes. Traditional ventilator systems often rely on static or rule-based approaches, which lack the flexibility required to handle dynamic patient-specific respiratory patterns. This study proposes a novel deep learning framework based on recurrent neural networks (RNNs) for accurate and real-time prediction of ventilator pressure. The proposed model integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures with an attention mechanism to effectively capture temporal dependencies in respiratory signals. In addition, a multimodal feature fusion strategy is employed to incorporate both physiological and contextual information, enhancing the model’s predictive capability. The framework is further optimized using advanced training strategies, including hybrid loss functions and adaptive optimization techniques. Experimental results demonstrate that the proposed approach outperforms traditional machine learning models and baseline deep learning architectures in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The model shows strong generalization ability and robustness, making it suitable for real-world healthcare applications. The findings of this research highlight the potential of integrating sequential modeling, attention mechanisms, and multimodal learning to develop intelligent and adaptive healthcare systems. The proposed framework provides a scalable and efficient solution for ventilator pressure prediction and lays the foundation for future advancements in AI-driven critical care technologies.