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

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dc.citedby0
dc.contributor.authorAl Barazanchi I.I.en_US
dc.contributor.authorHashim W.en_US
dc.contributor.authorThabit R.en_US
dc.contributor.authorAlrasheedy M.N.en_US
dc.contributor.authorAljohan A.en_US
dc.contributor.authorPark J.en_US
dc.contributor.authorChang B.en_US
dc.contributor.authorid57659035200en_US
dc.contributor.authorid11440260100en_US
dc.contributor.authorid58891173100en_US
dc.contributor.authorid58070638300en_US
dc.contributor.authorid59483892700en_US
dc.contributor.authorid59483484300en_US
dc.contributor.authorid15055487300en_US
dc.date.accessioned2025-03-03T07:46:07Z
dc.date.available2025-03-03T07:46:07Z
dc.date.issued2024
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.32604/cmc.2024.055079
dc.identifier.epage4832
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85212861544
dc.identifier.spage4787
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85212861544&doi=10.32604%2fcmc.2024.055079&partnerID=40&md5=91e45ab5feac90513688841a8fc6b5a5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36958
dc.identifier.volume81
dc.pagecount45
dc.publisherTech Science Pressen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleComputers, Materials and Continua
dc.subjectClinical research
dc.subjectDeep neural networks
dc.subjectDiagnosis
dc.subjectDiseases
dc.subjectElectrotherapeutics
dc.subjectHospital data processing
dc.subjectLong short-term memory
dc.subjectMultilayer neural networks
dc.subjectQuery languages
dc.subjectQuery processing
dc.subjectClinical decision support system
dc.subjectClinical decision support systems
dc.subjectDeep learning
dc.subjectHealthcare
dc.subjectLong short-term memory
dc.subjectMedical query
dc.subjectNeural-networks
dc.subjectRecurrent neural network
dc.subjectShort term memory
dc.subjectWireless body area network
dc.subjectConvolutional neural networks
dc.titleOptimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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