Open Access

ARTICLE

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

by Israa Ibraheem Al Barazanchi^1,2,*, Wahidah Hashim¹, Reema Thabit¹, Mashary Nawwaf Alrasheedy^3,4, Abeer Aljohan⁵, Jongwoon Park⁶, Byoungchol Chang⁶

1 College of Computing and Informatics, Universiti Tenaga Nasional (UNITEN), Kajang, 43000, Malaysia
2 College of Engineering, University of Warith Al-Anbiyaa, Karbala, 56001, Iraq
3 Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
4 Department of Computer Science, Applied College, University of Hail, Hail, 55424, Saudi Arabia
5 Department of Computer Science and Informatics, Taibah University, Medina, 42353, Saudi Arabia
6 Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea

* Corresponding Author: Israa Ibraheem Al Barazanchi. Email:

Computers, Materials & Continua 2024, 81(3), 4787-4832. https://doi.org/10.32604/cmc.2024.055079

Received 16 June 2024; Accepted 13 October 2024; Issue published 19 December 2024

Abstract

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.

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