Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natur...Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems(DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.展开更多
With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). We prop...With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). We propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS, and the conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS operate efficiently.展开更多
Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more w...Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.展开更多
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...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.展开更多
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 S...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.展开更多
Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecul...Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecular alterations.Recent advances in artificial intelligence(AI),particularly deep learning,have enabled the integration of diverse clinical data modalities to support diagnosis,treatment planning,and prognostication in bone oncology.This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging,digital pathology,multi-omics profiling,and electronic health records.We conducted a structured review of peer-reviewed literature published between 2015 and early 2025,focusing on the development,validation,and clinical applicability of AI models for BT diagnosis,subtyping,treatment response prediction,and recurrence monitoring.Although multimodal models have demonstrated advantages over unimodal approaches,especially in handling missing data and improving generalizability,most remain constrained by single-center study designs,small sample sizes,and limited prospective or external validation.Persistent technical and translational challenges include semantic misalignment across modalities,incomplete datasets,limited model interpretability,and regulatory and infrastructural barriers to clinical integration.To address these limitations,we highlight emerging directions such as contrastive representation learning,generative data augmentation,transformer-based fusion architectures,and privacy-preserving federated learning.We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability.In summary,multimodal AI represents a promising paradigm for advancing precision care in BTs.Realizing its full clinical potential will require methodologically rigorous,biologically informed,and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.展开更多
Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Locatio...Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Location choice is driven by diverse characteristics;including but not limited to environmental factors,access,services,and the socioeconomic status of a neighbourhood.This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a sevenfactor classification tool and an assessment of international property websites.Despite the availability of digital earth data,the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making.Based on a case study in Dublin,Ireland,we find that although neighbourhood digital earth data may be readily available to support decision making,the gap persists.We hypothesise that the reason is two-fold.Firstly,there is a technical challenge to transform location data into usable information.Secondly,the market may not wish to provide location information which can be perceived as negative.We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.展开更多
The simulation-based decision support system (SBDSS) is designed to achieve a highlevel of performance, flexibility and adaptability, in response to meet the special needs of productionand logistics management during ...The simulation-based decision support system (SBDSS) is designed to achieve a highlevel of performance, flexibility and adaptability, in response to meet the special needs of productionand logistics management during the economic system reform era in China. It consists two subsys-tems: the object library modeler (OLM) and the simulation engine and its manager (SEM). UsingSBDSS the decision makers can work out their optimal production choice under certain circumstancesthrough scenario simulations. And they can test a set of virtual organizations reflecting systems re-form before a real reorganization has been taken, as well as perform a virtual manufacturing processfor a new product design (Copyright @ 1998 IFAC).展开更多
Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechani...Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).展开更多
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 t...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.展开更多
The potential demand on financial risk management has being increased considerably by the reason of Basel 11 regulations and instabilities in economy. In recent years, financial institutions and companies have been st...The potential demand on financial risk management has being increased considerably by the reason of Basel 11 regulations and instabilities in economy. In recent years, financial institutions and companies have been struggled for building up intensive financial risk management tools due to Basel II guidance on establishing financial self-assessment systems. In this respect, decision support system has a significant role on effectuating intensive financial risk management roadmap. In this study, a reformative financial risk management system is presented with the combination of determining financial risks with their importance, calculating risk scores and making suggestions based on detected risk scores by applying corrective actions. First, financial risk factors and indicators of these risk variables are selected and weights of these variables are specified by using fuzzy goal programming. After that, total risk scores are calculated and amendatory financial activities are appeared by means of expertons method which also provides possibilities of the alternative decisions. To illustrate the performance of integrated and multistage decision support system, a survey is applied on the end users.展开更多
This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical ...This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.展开更多
Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical h...Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.展开更多
Incidental asymptomatic gallstone disease(AGD)is prevalent,but its management remains contentious.Old-fashioned conservative care is under scrutiny today with precision medicine and artificial intelligence(AI)on the h...Incidental asymptomatic gallstone disease(AGD)is prevalent,but its management remains contentious.Old-fashioned conservative care is under scrutiny today with precision medicine and artificial intelligence(AI)on the horizon.Unlike previous overviews,this review primarily focuses on clinical outcomes,surgical decision-making,and the integration of genomics,predictive analytics,and precision tools into AGD management.We emphasise how AI-based models and precision diagnostics enable tailored recommendations,preventing unnecessary cholecystectomy in low-risk patients while requiring early elective surgery in high-risk subgroups(e.g.,single large stones,polyps,endemic cancer areas).We also compare cost-effectiveness,surgical safety,and quality of life(QoL)measures within this precision strategy.Our vision is to overcome the binary"operate or observe"model by leveraging technology-enabled forecasting and collaborative decision-making to deliver future-proof care for AGD.The terminology of asymptomatic gallstones was used more meticulously in the era of open cholecystectomy,when neither the diagnostic tools nor the concept of minimal access surgery were available.After careful consideration of the evidence on natural history,risk of surgery,QoL,and cost,we recommend that clinicians utilise shared decision-making and present information regarding cholecystectomy as an intervention option to all patients with asymptomatic gallstones.展开更多
Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as L...Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.展开更多
Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across var...Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across various diagnostic domains,emphasizing its role in improving clinical decision-making.The evolution of medical diagnostics from traditional observational methods to sophisticated imaging,laboratory tests,and molecular diagnostics lays the foundation for understanding AI’s impact.Modern diagnostics are inherently complex,influenced by multifactorial disease presentations,patient variability,cognitive biases,and systemic factors like data overload and interdisciplinary collaboration.AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches,employing machine learning and deep learning algorithms to analyze vast datasets,identify patterns,and generate accurate differential diagnoses.AI’s potential in diagnostics is demonstrated through applications in genomics,predictive analytics,and early disease detection,with successful case studies in oncology,radiology,pathology,ophthalmology,dermatology,gastroenterology,and psychiatry.These applications demonstrate AI’s ability to process complex medical data,facilitate early intervention,and extend specialized care to underserved populations.However,integrating AI into diagnostics faces significant limitations,including technical challenges related to data quality and system integration,regulatory hurdles,ethical concerns about transparency and bias,and risks of misinformation and overreliance.Addressing these challenges requires robust regulatory frameworks,ethical guidelines,and continuous advancements in AI technology.The future of AI in diagnostics promises further innovations in multimodal AI,genomic data integration,and expanding access to high-quality diagnostic services globally.Responsible and ethical implementation of AI will be crucial to fully realize its potential,ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes.This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics,advocating for sustained investment and collaborative efforts to harness its benefits effectively.展开更多
This article evaluates the transformative potential of large language models(LLMs)as patient education tools for managing inflammatory bowel disease.The discussion highlights their ability to deliver nuanced and perso...This article evaluates the transformative potential of large language models(LLMs)as patient education tools for managing inflammatory bowel disease.The discussion highlights their ability to deliver nuanced and personalized infor-mation,addressing limitations in traditional educational materials.Key consider-ations include the necessity for domain-specific fine-tuning to enhance accuracy,the adoption of robust evaluation metrics beyond readability,and the integration of LLMs with clinical decision support systems to improve real-time patient education.Ethical and accessibility challenges,such as algorithmic bias,data privacy,and digital literacy,are also examined.Recommendations emphasize the importance of interdisciplinary collaboration to optimize LLM integration,en-suring equitable access and improved patient outcomes.By advancing LLM technology,healthcare can empower patients with accurate and personalized information,enhancing engagement and disease management.展开更多
BACKGROUND:Rapid and accurate identification of high-risk patients in the emergency departments(EDs)is crucial for optimizing resource allocation and improving patient outcomes.This study aimed to develop an early pre...BACKGROUND:Rapid and accurate identification of high-risk patients in the emergency departments(EDs)is crucial for optimizing resource allocation and improving patient outcomes.This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.METHODS:This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage,Assessment,and Treatment(CETAT)database,which was collected between January 1^(st),2020,and June 25^(th),2023.The primary outcome was the identification of high-risk patients needing immediate treatment.Various machine learning methods,including a deep-learningbased multilayer perceptron(MLP)classifier were evaluated.Model performance was assessed using the area under the receiver operating characteristic curve(AUC-ROC).AUC-ROC values were reported for three scenarios:a default case,a scenario requiring sensitivity greater than 0.8(Scenario I),and a scenario requiring specificity greater than 0.8(Scenario II).SHAP values were calculated to determine the importance of each predictor within the MLP model.RESULTS:A total of 38,797 patients were analyzed,of whom 18.2%were identified as high-risk.Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738,with the MLP model outperforming logistic regression(LR),Gaussian Naive Bayes(GNB),and the National Early Warning Score(NEWS).SHAP value analysis identified coma state,peripheral capillary oxygen saturation(SpO_(2)),and systolic blood pressure as the top three predictive factors in the MLP model,with coma state exerting the most contribution.CONCLUSION:Compared with other methods,the MLP model with initial vital signs demonstrated optimal prediction accuracy,highlighting its potential to enhance clinical decision-making in triage in the EDs.展开更多
基金co-financed by the European Union(European Social Fund-ESF)and Greek national funds through the Operational Program‘‘Education and Lifelong Learning’’of the National Strategic Reference Framework(NSRF)-Research Funding Program:Thales.Investing in knowledge society through the European Social Fund
文摘Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems(DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.
基金This project was supported by the Teaching and Research Award Fund for Outstanding Young Teachers in Higher Education Institutions of MOE.
文摘With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). We propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS, and the conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS operate efficiently.
文摘Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.
文摘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.
基金Lan-Fang Qin was supported by National Innovation and Entrepreneurship Training Program for College Students(2022KYCX69)Rui Wang was supported by the Nursing Subject(Zhejiang Province"13th Five-Year Plan"Characteristic Specialty Construction Project)under Grant(JY30001)Chong-Bin Liu supported by the grants from National Natural Science Foundation of Zhejiang Province,No.LY21H260005 and No.2017290-40.
文摘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.
基金supported by the National Natural Science Foundation of China[Grant No.:82172524]the Natural Science Foundation of Hubei Province[Grant No.:2025AFB240].
文摘Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecular alterations.Recent advances in artificial intelligence(AI),particularly deep learning,have enabled the integration of diverse clinical data modalities to support diagnosis,treatment planning,and prognostication in bone oncology.This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging,digital pathology,multi-omics profiling,and electronic health records.We conducted a structured review of peer-reviewed literature published between 2015 and early 2025,focusing on the development,validation,and clinical applicability of AI models for BT diagnosis,subtyping,treatment response prediction,and recurrence monitoring.Although multimodal models have demonstrated advantages over unimodal approaches,especially in handling missing data and improving generalizability,most remain constrained by single-center study designs,small sample sizes,and limited prospective or external validation.Persistent technical and translational challenges include semantic misalignment across modalities,incomplete datasets,limited model interpretability,and regulatory and infrastructural barriers to clinical integration.To address these limitations,we highlight emerging directions such as contrastive representation learning,generative data augmentation,transformer-based fusion architectures,and privacy-preserving federated learning.We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability.In summary,multimodal AI represents a promising paradigm for advancing precision care in BTs.Realizing its full clinical potential will require methodologically rigorous,biologically informed,and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.
基金Hamidreza Rabiei-Dastjerdi is a Marie Skłodowska-Curie Career-FIT Fellow at the UCD School of Computer Science and CeADAR(Ireland’s National Centre for Applied Data Analytics&AI)Career-FIT has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.713654.
文摘Searching for a property is inherently a multicriteria spatial decision.The decision is primarily based on three high-level criteria composed of household needs,building facilities,and location characteristics.Location choice is driven by diverse characteristics;including but not limited to environmental factors,access,services,and the socioeconomic status of a neighbourhood.This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a sevenfactor classification tool and an assessment of international property websites.Despite the availability of digital earth data,the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making.Based on a case study in Dublin,Ireland,we find that although neighbourhood digital earth data may be readily available to support decision making,the gap persists.We hypothesise that the reason is two-fold.Firstly,there is a technical challenge to transform location data into usable information.Secondly,the market may not wish to provide location information which can be perceived as negative.We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.
文摘The simulation-based decision support system (SBDSS) is designed to achieve a highlevel of performance, flexibility and adaptability, in response to meet the special needs of productionand logistics management during the economic system reform era in China. It consists two subsys-tems: the object library modeler (OLM) and the simulation engine and its manager (SEM). UsingSBDSS the decision makers can work out their optimal production choice under certain circumstancesthrough scenario simulations. And they can test a set of virtual organizations reflecting systems re-form before a real reorganization has been taken, as well as perform a virtual manufacturing processfor a new product design (Copyright @ 1998 IFAC).
文摘Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).
基金This project was supported by the Development and application of nursing decision support system based on artificial intelligence(No.2019ZD006).
文摘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.
文摘The potential demand on financial risk management has being increased considerably by the reason of Basel 11 regulations and instabilities in economy. In recent years, financial institutions and companies have been struggled for building up intensive financial risk management tools due to Basel II guidance on establishing financial self-assessment systems. In this respect, decision support system has a significant role on effectuating intensive financial risk management roadmap. In this study, a reformative financial risk management system is presented with the combination of determining financial risks with their importance, calculating risk scores and making suggestions based on detected risk scores by applying corrective actions. First, financial risk factors and indicators of these risk variables are selected and weights of these variables are specified by using fuzzy goal programming. After that, total risk scores are calculated and amendatory financial activities are appeared by means of expertons method which also provides possibilities of the alternative decisions. To illustrate the performance of integrated and multistage decision support system, a survey is applied on the end users.
文摘This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.
文摘Acute appendicitis(AAp)remains one of the most common abdominal emergencies,requiring rapid and accurate diagnosis to prevent complications and unnecessary surgeries.Conventional diagnostic methods,including medical history,clinical assessment,biochemical markers,and imaging techniques,often present limitations in sensitivity and specificity,especially in atypical cases.In recent years,artificial intelligence(AI)has demonstrated remarkable potential in enhancing diagnostic accuracy through machine learning(ML)and deep learning(DL)models.This review evaluates the current applications of AI in both adult and pediatric AAp,focusing on clinical data-based models,radiological imaging analysis,and AI-assisted clinical decision support systems.ML models such as random forest,support vector machines,logistic regression,and extreme gradient boosting have exhibited superior diagnostic performance compared to traditional scoring systems,achieving sensitivity and specificity rates exceeding 90%in multiple studies.Additionally,DL techniques,particularly convolutional neural networks,have been shown to outperform radiologists in interpreting ultrasound and computed tomography images,enhancing diagnostic confidence.This review synthesized findings from 65 studies,demonstrating that AI models integrating multimodal data including clinical,laboratory,and imaging parameters further improved diagnostic precision.Moreover,explainable AI approaches,such as SHapley Additive exPlanations and local interpretable model-agnostic explanations,have facilitated model transparency,fostering clinician trust in AI-driven decision-making.This review highlights the advancements in AI for AAp diagnosis,emphasizing that AI is used not only to establish the diagnosis of AAp but also to differentiate complicated from uncomplicated cases.While preliminary results are promising,further prospective,multicenter studies are required for large-scale clinical implementation,given that a great proportion of current evidence derives from retrospective designs,and existing prospective cohorts exhibit limited sample sizes or protocol variability.Future research should also focus on integrating AI-driven decision support tools into routine emergency care workflows.
文摘Incidental asymptomatic gallstone disease(AGD)is prevalent,but its management remains contentious.Old-fashioned conservative care is under scrutiny today with precision medicine and artificial intelligence(AI)on the horizon.Unlike previous overviews,this review primarily focuses on clinical outcomes,surgical decision-making,and the integration of genomics,predictive analytics,and precision tools into AGD management.We emphasise how AI-based models and precision diagnostics enable tailored recommendations,preventing unnecessary cholecystectomy in low-risk patients while requiring early elective surgery in high-risk subgroups(e.g.,single large stones,polyps,endemic cancer areas).We also compare cost-effectiveness,surgical safety,and quality of life(QoL)measures within this precision strategy.Our vision is to overcome the binary"operate or observe"model by leveraging technology-enabled forecasting and collaborative decision-making to deliver future-proof care for AGD.The terminology of asymptomatic gallstones was used more meticulously in the era of open cholecystectomy,when neither the diagnostic tools nor the concept of minimal access surgery were available.After careful consideration of the evidence on natural history,risk of surgery,QoL,and cost,we recommend that clinicians utilise shared decision-making and present information regarding cholecystectomy as an intervention option to all patients with asymptomatic gallstones.
文摘Artificial intelligence(AI)-augmented contrast-enhanced ultrasonography(CEUS)is emerging as a powerful tool in liver imaging,particularly in enhancing the accuracy of Liver Imaging Reporting and Data System(known as LI-RADS)classi-fication.This review synthesized published data on the integration of machine learning and deep learning techniques into CEUS,revealing that AI algorithms can improve the detection and quantification of contrast enhancement patterns.Such improvements led to more consistent LI-RADS categorization,reduced interoperator variability,and enabled real-time analysis that streamlined work-flow.The enhanced sensitivity of AI tools facilitated better differentiation between benign and malignant lesions,ultimately optimizing patient management.These advances suggest that AI-augmented CEUS could transform liver imaging by providing rapid,reliable,and objective assessments.However,the review also highlighted the need for further large-scale,multicenter studies to fully validate these findings and ensure the safe integration of AI into routine clinical practice.INTRODUCTION International hepatology society guidelines have established contrast-enhanced computed tomography(CT)and contrast-enhanced magnetic resonance imaging(MRI)as the imaging modalities of choice for diagnosing hepatocellular carcinoma(HCC)lesions larger than 1 cm.MRI remains the gold standard for detecting small HCC nodules in cirrhotic livers due to its superior soft-tissue contrast and functional imaging capabilities.However,early or atypical presentations remain challenging for differential diagnosis,staging,and treatment planning.In these scenarios contrast-enhanced ultrasonography(CEUS)is a valuable second-line tool,offering real-time,radiation-free evaluation and repeatability for follow-up.A recent meta-analysis of head-to-head studies reported comparable diagnostic performance between CEUS and CT/MRI with pooled sensitivities and specificities of 0.67/0.88 for CEUS vs 0.60/0.98 for CT/MRI in non-HCC malignancies,and similar specificities for HCC diagnosis(0.70 for CEUS vs 0.59 for CT;0.81 for CEUS vs 0.79 for MRI)[1].Given the limitations of individual imaging modalities,hybrid techniques and multimodal approaches are gaining traction for improving lesion detection,especially in cases where standard methods fall short.Artificial intelligence(AI)has emerged as a powerful tool in medical imaging,enhancing diagnostic accuracy and reliability across platforms.In CEUS liver imaging dynamic enhancement patterns often challenge consistent interpretation across observers.AI holds particular promise for standardizing assessments.The growing complexity of liver tumor evaluation has also driven interest in approaches that integrate serum bio-markers with advanced imaging.However,no single strategy currently meets all the diagnostic and prognostic re-quirements.Recent studies highlighted the potential of AI to bridge this gap by enabling precise image interpretation and facilitating the integration of heterogeneous clinical and imaging data[2].Altogether the convergence of CEUS with AI and radiomics offers a dynamic,quantitative,and potentially reproducible paradigm for liver lesion assessment,comple-menting traditional imaging methods.This review aimed to provide an overview of current advances in AI-driven CEUS for liver lesion assessment with a particular focus on automated Liver Imaging Reporting and Data System(LI-RADS)classification,radiomics-based models,and future clinical integration.While another recent systematic review[3]provided a comprehensive analysis of AI applications in CEUS,our approach offers a targeted perspective,emphasizing LI-RADS-centered scoring,automated lesion characterization,and clinical utility,particularly in the context of HCC diagnosis and management.In the methodological process of this narrative mini-review,the literature selection was primarily based on targeted PubMed searches.ChatGPT-4o(OpenAI)[4]was employed to assist in refining query parameters and identifying relevant,up-to-date peer-reviewed sources on CEUS-based AI applications.
文摘Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across various diagnostic domains,emphasizing its role in improving clinical decision-making.The evolution of medical diagnostics from traditional observational methods to sophisticated imaging,laboratory tests,and molecular diagnostics lays the foundation for understanding AI’s impact.Modern diagnostics are inherently complex,influenced by multifactorial disease presentations,patient variability,cognitive biases,and systemic factors like data overload and interdisciplinary collaboration.AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches,employing machine learning and deep learning algorithms to analyze vast datasets,identify patterns,and generate accurate differential diagnoses.AI’s potential in diagnostics is demonstrated through applications in genomics,predictive analytics,and early disease detection,with successful case studies in oncology,radiology,pathology,ophthalmology,dermatology,gastroenterology,and psychiatry.These applications demonstrate AI’s ability to process complex medical data,facilitate early intervention,and extend specialized care to underserved populations.However,integrating AI into diagnostics faces significant limitations,including technical challenges related to data quality and system integration,regulatory hurdles,ethical concerns about transparency and bias,and risks of misinformation and overreliance.Addressing these challenges requires robust regulatory frameworks,ethical guidelines,and continuous advancements in AI technology.The future of AI in diagnostics promises further innovations in multimodal AI,genomic data integration,and expanding access to high-quality diagnostic services globally.Responsible and ethical implementation of AI will be crucial to fully realize its potential,ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes.This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics,advocating for sustained investment and collaborative efforts to harness its benefits effectively.
文摘This article evaluates the transformative potential of large language models(LLMs)as patient education tools for managing inflammatory bowel disease.The discussion highlights their ability to deliver nuanced and personalized infor-mation,addressing limitations in traditional educational materials.Key consider-ations include the necessity for domain-specific fine-tuning to enhance accuracy,the adoption of robust evaluation metrics beyond readability,and the integration of LLMs with clinical decision support systems to improve real-time patient education.Ethical and accessibility challenges,such as algorithmic bias,data privacy,and digital literacy,are also examined.Recommendations emphasize the importance of interdisciplinary collaboration to optimize LLM integration,en-suring equitable access and improved patient outcomes.By advancing LLM technology,healthcare can empower patients with accurate and personalized information,enhancing engagement and disease management.
基金Applicable Funding Source University of Science and Technology of China(to YLL)National Natural Science Foundation of China(12126604)(to MPZ)+1 种基金R&D project of Pazhou Lab(Huangpu)(2023K0609)(to MPZ)Anhui Provincial Natural Science(grant number 2208085MH235)(to KJ)。
文摘BACKGROUND:Rapid and accurate identification of high-risk patients in the emergency departments(EDs)is crucial for optimizing resource allocation and improving patient outcomes.This study aimed to develop an early prediction model for identifying high-risk patients in EDs using initial vital sign measurements.METHODS:This retrospective cohort study analyzed initial vital signs from the Chinese Emergency Triage,Assessment,and Treatment(CETAT)database,which was collected between January 1^(st),2020,and June 25^(th),2023.The primary outcome was the identification of high-risk patients needing immediate treatment.Various machine learning methods,including a deep-learningbased multilayer perceptron(MLP)classifier were evaluated.Model performance was assessed using the area under the receiver operating characteristic curve(AUC-ROC).AUC-ROC values were reported for three scenarios:a default case,a scenario requiring sensitivity greater than 0.8(Scenario I),and a scenario requiring specificity greater than 0.8(Scenario II).SHAP values were calculated to determine the importance of each predictor within the MLP model.RESULTS:A total of 38,797 patients were analyzed,of whom 18.2%were identified as high-risk.Comparative analysis of the predictive models for high-risk patients showed AUC-ROC values ranging from 0.717 to 0.738,with the MLP model outperforming logistic regression(LR),Gaussian Naive Bayes(GNB),and the National Early Warning Score(NEWS).SHAP value analysis identified coma state,peripheral capillary oxygen saturation(SpO_(2)),and systolic blood pressure as the top three predictive factors in the MLP model,with coma state exerting the most contribution.CONCLUSION:Compared with other methods,the MLP model with initial vital signs demonstrated optimal prediction accuracy,highlighting its potential to enhance clinical decision-making in triage in the EDs.
基金supported by the National High Technology Research and Development Program(863 program)of China(2012AA101906-2)the National Natural Science Foundation of China(3140030594)
