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

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.

Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System(CDSS).Generally,CDSS is used to predict the individuals’heart disease and periodically update the condition of the patients.This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers.Here,the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease.Then,the optimization is achieved using the Adam Optimizer(AO)model with the publicly available dataset known as the Stalog dataset.This flowis used to construct the model,and the evaluation is done with various prevailing approaches like Decision tree,Random Forest,Logistic Regression,Naive Bayes and so on.The statistical analysis is done with theWilcoxon rank-summethod for extracting the p-value of the model.The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy.This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage.Thus,the earlier treatment process helps to eliminate the death rate.Here,simulation is done withMATLAB 2016b,and metrics like accuracy,precision-recall,F-measure,p-value,ROC are analyzed to show the significance of the model.

Progress of clinical decision support systems in stroke nursing care

Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent computer system,can assist nurses in decision-mak-ing to collect information quickly,make the most suitable personalized decisions for patients,and improve nurses’decision-making judgment and quality of care.Promoting the development and application of decision support sys-tems in stroke nursing significantly enhances the nursing staff’s work quality and patients’prognosis.Therefore,this paper reviews the research progress of domestic and international clinical decision support systems in stroke nursing care to provide other researchers with specific research directions for developing and applying decision support systems in stroke nursing care.

Bibliometrics analysis of clinical decision support systems research in nursing

Objective:Artificial intelligence(AI)has a big impact on healthcare now and in the future.Nurses play an important role in the medical field and will benefit greatly from this technology.AI-Enabled Clinical Decision Support Systems have received a great deal of attention recently.Bibliometric analysis can offer an objective,systematic,and comprehensive analysis of a specific field with a vast background.However,no bibliometric analysis has investigated AI-enabled clinical decision support systems research in nursing.The purpose of research to determine the characteristics of articles about the global performance and development of AI-enabled clinical decision support systems research in nursing.Methods:In this study,the bibliometric approach was used to estimate the searched data on clinical decision support systems research in nursing from 2009 to 2022,and we also utilized CiteSpace and VOSviewer software to build visualizing maps to assess the contribution of different journals,authors,et al.,as well as to identify research hot spots and promising future trends in this research field.Result:From 2009 to 2022,a total of 2,159 publications were retrieved.The number of publications and citations on AI-enabled clinical decision support systems research in nursing has increased obvious ly in recent years.However,they are understudied in the field of nursing and there is a compelling need to develop more high-quality research.Conclusion:AI-Enabled Nursing Decision Support System use in clinical practice is still in its early stages.These analyses and results hope to provide useful information and references for future research directions for researchers and nursing practitioners who use AI-enabled clinical decision support systems.

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

In recent years, advanced magnetic resonance imaging(MRI) techniques, such as magnetic resonance spec-troscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic prob-lems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical deci-sion support systems(CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually inticle is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be intro-duced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

Potential Usefulness of Diagnostic Reminder as Web-based Clinical Decision Support System

Diagnostic error is prevalent and there is a need for reducing it for improving patient safety. Electronic resources may be candidates as diagnostic decision support systems to assist physicians in clinics or hospitals. A unique system has been developed by consisting of a disease knowledge database coupled with algorithms designed specifically for clinical reminders during real-time diagnostic processes. This system is currently being used as a diagnostic decision-support tool in a clinic base and its usefulness has been empirically evaluated by applying it to the case reports in the New England Journal of Medicine. Further studies are needed to prove its usefulness for reducing diagnostic errors in real clinical practice.

A clinical decision support system using rough set theory and machine learning for disease prediction

Objective Technological advances have led to drastic changes in daily life,and particularly healthcare,while traditional diagnosis methods are being replaced by technology-oriented models and paper-based patient health-care records with digital files.Using the latest technology and data mining techniques,we aimed to develop an automated clinical decision support system(CDSS),to improve patient prognoses and healthcare delivery.Our proposed approach placed a strong emphasis on improvements that meet patient,parent,and physician expec-tations.We developed a flexible framework to identify hepatitis,dermatological conditions,hepatic disease,and autism in adults and provide results to patients as recommendations.The novelty of this CDSS lies in its inte-gration of rough set theory(RST)and machine learning(ML)techniques to improve clinical decision-making accuracy and effectiveness.Methods Data were collected through various web-based resources.Standard preprocessing techniques were applied to encode categorical features,conduct min-max scaling,and remove null and duplicate entries.The most prevalent feature in the class and standard deviation were used to fill missing categorical and continuous feature values,respectively.A rough set approach was applied as feature selection,to remove highly redundant and irrelevant elements.Then,various ML techniques,including K nearest neighbors(KNN),linear support vector machine(LSVM),radial basis function support vector machine(RBF SVM),decision tree(DT),random forest(RF),and Naive Bayes(NB),were employed to analyze four publicly available benchmark medical datasets of different types from the UCI repository and Kaggle.The model was implemented in Python,and various validity metrics,including precision,recall,F1-score,and root mean square error(RMSE),applied to measure its performance.Results Features were selected using an RST approach and examined by RF analysis and important features of hepatitis,dermatology conditions,hepatic disease,and autism determined by RST and RF exhibited 92.85%,90.90%,100%,and 80%similarity,respectively.Selected features were stored as electronic health records and various ML classifiers,such as KNN,LSVM,RBF SVM,DT,RF,and NB,applied to classify patients with hepatitis,dermatology conditions,hepatic disease,and autism.In the last phase,the performance of proposed classifiers was compared with that of existing state-of-the-art methods,using various validity measures.RF was found to be the best approach for adult screening of:hepatitis with accuracy 88.66%,precision 74.46%,recall 75.17%,F1-score 74.81%,and RMSE value 0.244;dermatology conditions with accuracy 97.29%,precision 96.96%,recall 96.96%,F1-score 96.96%,and RMSE value,0.173;hepatic disease,with accuracy 91.58%,precision 81.76%,recall 81.82%,F1-Score 81.79%,and RMSE value 0.193;and autism,with accuracy 100%,precision 100%,recall 100%,F1-score 100%,and RMSE value 0.064.Conclusion The overall performance of our proposed framework may suggest that it could assist medical experts in more accurately identifying and diagnosing patients with hepatitis,dermatology conditions,hepatic disease,and autism.

Applying the Technology Acceptance Model (TAM) in Information Technology System to Evaluate the Adoption of Decision Support System

With the beginning of the information systems’ spreading, people started thinking about using them for making business decisions. Computer technology solutions, such as the Decision Support System, make the decision-making process less complex and simpler for problem-solving. In order to make a high-quality business decision, managers need to have a great deal of appropriate information. Nonetheless, this complicates the process of making appropriate decisions. In a situation like that, the possibility of using DSS is quite logical. The aim of this paper is to find out the intended use of DSS for medium and large business organizations in USA by applying the Technology Acceptance Model (TAM). Different models were developed in order to understand and predict the use of information systems, but the information systems community mostly used TAM to ensure this issue. The purpose of the research model is to determine the elements of analysis that contribute to these results. The sample for the research consisted of the target group that was supposed to have completed an online questionnaire about the manager’s use of DSS in medium and large American companies. The information obtained from the questionnaires was analyzed through the SPSS statistical software. The research has indicated that, this is primarily used due to a significant level of Perceived usefulness and For the Perceived ease of use.

Evaluation of a Specialized Nurse Decision Support System in the Prevention of Stroke- Associated Pneumonia

Objective:To design and implement a specialized nurse decision support system in the Department of Neurology and explore its effectiveness in preventing stroke-associated pneumonia(SAP).Methods:A decision support module for specialized nurses was developed based on SAP-graded prevention strategies.A total of 664 neurology inpatients admitted to The First People’s Hospital of Xuzhou between July 2023 and September 2023 were selected as the conventional group,receiving standard nursing care.Another 704 neurology inpatients admitted between October 2023 and December 2023 were selected as the experimental group,receiving SAP-graded prevention strategies under the specialized nurse decision support system.The incidence of SAP in the two groups was compared.The occurrence of SAP was recorded using the Acute Ischemic Stroke-Associated Pneumonia Risk(A2DS2)scoring system.Swallowing function was evaluated using the Water Swallow Test(WST),and quality of life was assessed using the Swallowing Quality of Life(SWAL-QOL)scale.Results:The incidence of SAP in the experimental group was significantly lower than in the conventional group(P<0.05).After nursing interventions,the WST scores in the experimental group were lower,while the SWAL-QOL scores were higher compared to the conventional group(P<0.05).Conclusion:The design and implementation of a specialized nurse decision support system in the Department of Neurology significantly reduced the incidence of SAP in neurology inpatients,improved swallowing function,and enhanced quality of life.This approach shows promise for widespread application.

Research on the Application of Artificial Intelligence in Management Accounting Decision Support Systems

The rapid development of the digital economy,driven by artificial intelligence(AI),is profoundly transforming traditional accounting practices and business models.The emergence of innovative models such as“wisdom+accounting”and“wisdom+financial sharing”has opened new avenues for enhancing enterprise decision-making support systems.This paper delves into the application of AI technology in accounting,examining its practical implementation and associated challenges.To mitigate potential risks arising from technological advancements,enterprises should establish robust and efficient intelligent financial systems.Additionally,organizations should foster a mindset of change within their accounting teams,improve the application of management information systems,strengthen internal control mechanisms,and continuously upgrade intelligent accounting software.Financial managers must adapt to the evolving landscape and proactively adjust their career paths and development strategies.

Design and Implementation of the Employment Management Decision Support System based on Machine Learning

To address the challenges of current college student employment management,this study designed and implemented a machine learning-based decision support system for college student employment management.The system collects and analyzes multidimensional data,uses machine learning algorithms for prediction and matching,provides personalized employment guidance for students,and provides decision support for universities and enterprises.The research results indicate that the system can effectively improve the efficiency and accuracy of employment guidance,promote school-enterprise cooperation,and achieve a win-win situation for all parties.

An Intelligent Decision Support System Generator

The focus of this paper is on a new concept framework and an architecture of an intelligent decision support syetem generator (DSSG). The framework results from a synthesis of two existing frameworks: Spragae and Bonczek, while the architecture is a rooted partial order network. From our experience which comes out of the project of DSSG, we consider that they are keys of further research and development of DSS.

Method of Establishing Object-Oriented System Structure for Decision Support System

In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.

Method for designing organization decision support system framework

The concept of organization decision support system （ODSS） is defined according to practical applications and novel understanding. And a framework for ODSS is designed. The framework has three components： infrastructure, decision-making process and decision execution process. Infrastructure is responsible to transfer data and information. Decision-making process is the ODSS＇s soul to support decision-making. Decision execution process is to evaluate and execute decision results derived from decision-making process. The framework presents a kind of logic architecture. An example is given to verify and analyze the framework. The analysis shows that the framework has practical values, and has also reference values for understanding ODSS and for theoretical studies.

Research and Application of Maize Precision Intelligence Spatial Decision Support System

In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.

Study on the Decision Support System for Northing of Winter Wheat Cultivation in Hebei Province

[Objective] This study was to provide methods to improve the scientificity and informatization level of agricultural decision-making system based on the study of Decision Support System for ＂Northing of Winter Wheat＂ in Hebei Province （DSS- NWWH）. [Method] The functions, development process, operation guidance as well as input and output modes of DSSNWWH were introduced, and the simulated results of the system were verified by comparing with the actual situations. [Result] The decision support system established in this study could predict whether a wheat variety could live through the winter in a certain area of northern Hebei Province, as well as the growth conditions based on the previous meteorological data or local weather forecast, and provided corresponding cultivation and management measures, making it possible for the user to determine whether the variety could be planted in the region based on the predictions. [Conclusion] The established DSSNWWH in this study can effectively help decision makers make decisions, providing scientific instructions for the northing of winter wheat.

Nursing decision support system:application in electronic health records

The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.

DEVELOPING EXPERT DECISION SUPPORT SYSTEM WTH VP-EXPERT AND LOTUS 1-2-3 IN RE-BLENDING BY USE

An expert decision support system (EDSS) for multi-bins balance and contro1 of orequality in production ore bins of some large-scale open pit iron mine in China has been developed byexpert svitem tool software VP-EXPERT and integration software LOTUS 1-2-3 in this paper. Itis known by practicing that a medium-scale EDSS constructed on microcomputer is completcly, feaasible by means of VP-EXEPERT to construct knowledge base system (KBS), LOTUS 1-2-3 tomake decision support system (DSS) and link them with BAT.

Disaster Reduction Decision Support System Against Debris Flows and Landslides Along Highway in Mountainous Area

Highways in mountainous areas are easy to be damaged by such natural disasters as debris flows and landslides and disaster reduction decision support system (DRDSS) is one of the important means to mitigate these disasters. Guided by the theories and technologies of debris flow and landslide reduction and supported by geographical information system (GIS), remote sensing and database techniques, a DRDSS against debris flow and landslide along highways in mountainous areas has been established on the basis of such principles as pertinence, systematicness, effectiveness, easy to use, open and expandability. The system consists of database, disaster analysis models and decisions on reduction of debris flows and landslides, mainly functioning to zone disaster dangerous degree, analyze debris flow activity, simulate debris flow deposition and diffusion, analyze landslide stability, select optimal highway renovation scheme and plan disaster prevention and control engineering. This system has been applied successfully to the debris flow and landslide treatment works along Palongzangbu Section of Sichuan-Tibet Highway.