Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

• Main Authors: Al Barazanchi I.I., Hashim W., Thabit R., Alrasheedy M.N., Aljohan A., Park J., Chang B.
• Other Authors: 57659035200
• Format: Article
• Published: Tech Science Press 2025
• Subjects: Clinical research; Deep neural networks; Diagnosis; Diseases; Electrotherapeutics; Hospital data processing; Long short-term memory; Multilayer neural networks; Query languages; Query processing; Clinical decision support system; Clinical decision support systems; Deep learning; Healthcare; Medical query; Neural-networks; Recurrent neural network; Short term memory; Wireless body area network; Convolutional neural networks

This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data imbalances, resulting in suboptimal accuracy and delayed decisions. Our goal is to develop Artificial Intelligence (AI) models that address these shortcomings, offering robust, real-time diagnostic support. We propose a hybrid RNN model that integrates SimpleRNN, LSTM layers, and echo state cells to manage long-term dependencies effectively. Additionally, we introduce CG-Net, a novel Convolutional Neural Network (CNN) framework for gastrointestinal disease classification, which outperforms traditional CNN models. We further enhance model performance through data augmentation and transfer learning, improving generalization and robustness against data scarcity and imbalance. Comprehensive validation, including 5-fold cross-validation and metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC), confirms the models? reliability. Moreover, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are employed to improve model interpretability. Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency, offering substantial advancements in WBANs and CDSS. Copyright ? 2024 The Authors. Published by Tech Science Press.