检索结果-内蒙古大学图书馆

Simulation Winter Conference

作者： Moustafa Abdelwanis Eman Ouda Andrei Sleptchenko Adriana F. Gabor mecit can emre simsekler Mohammed Omar Department of Management Science and Engineering Khalifa University Abu Dhabi UAE Department of Mathematics Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper investigates cross-training policies to enhance emergency department resilience in managing patient demand surges. We evaluated operational performance under varying patient flows using a simulation model based on an emergency department in Abu Dhabi, UAE. Results demonstrated a significant increase in patient length of stay in the triage room due to a shortage of available nurses during surges. Conversely, nurses in other areas (adult zone, pediatrics, and fast-track) were less affected. Two cross-training policies were investigated: pooling nurses from the triage and adult zones and expanding the pool to include the pediatrics section. The first policy reduced patient length of stay in the triage by 91.71%. The second policy further improved flexibility, leading to reductions in length of stay. Additional experiments revealed the limitations of the second policy when subjected to higher surge levels; therefore, further research is needed to explore a broader range of policies and contexts.

关键词： Surges Resilience

来源：评论

学校读者我要写书评

暂无评论

How resilient is your emergency department to inflow surges? A novel multidimensional framework for resilience enhancement

引用

SIMULATION MODELLING PRACTICE AND THEORY 2025年 142卷

作者： Ouda, Eman Sleptchenko, Andrei simsekler, mecit can emre Khalifa Univ Sci & Technol Dept Management Sci & Engn Abu Dhabi U Arab Emirates

Emergency Departments (EDs) encounter numerous operational challenges, uncertainties, and sudden surges in patient arrivals, often resulting in overcrowding. This overcrowding can impact patient outcomes, staff satisfaction, and the overall functionality of the system. Therefore, it is crucial to strengthen the resilience of these departments in the face of such uncertainties. In this study, we aim to identify the key factors contributing to ED overcrowding and develop a comprehensive hierarchical multidimensional resilience model. This model categorizes the components of three crowding assessment tools: NEDOCS, EDWIN, and READI, into two main categories, recoverability and resistance. The proposed approach demonstrates promising results as we apply it to examine three case studies using Discrete Event Simulation (DES). The subsequent phase involves providing strategic recommendations to improve the ED's performance and serves as a valuable tool for proactive system failure prevention. These recommendations encompass augmenting available resources and optimizing patient pathways, all aimed at enhancing the ED's ability to operate effectively. Our findings underscore the accuracy of the DES model in predicting the ED system's performance under various conditions, ranging from normal patient influx scenarios to high patient influx scenarios. This generalizable hierarchical resilience model aids decision-makers in comprehending system factors for better resource allocation and management decisions.

关键词： Emergency department Mass casualty incident Crowding scores Healthcare Discrete event simulation Multidimensional resilience

来源：评论

学校读者我要写书评

暂无评论

Exploring the role of safety culture dimensions in patient safety using a Bayesian Belief Network model

引用

SAFETY SCIENCE 2025年 186卷

作者： simsekler, mecit can emre Qazi, Abroon Al Ozonoff, Al Khalifa Univ Sci & Technol Dept Management Sci & Engn POB 127788 Abu Dhabi 127788 U Arab Emirates Boston Childrens Hosp Precis Vaccines Program 300 Longwood Ave Boston MA 02115 USA Amer Univ Sharjah Sch Business Adm Sharjah 26666 U Arab Emirates Harvard Med Sch Dept Pediat 25 Shattuck St Boston MA 02115 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA

There is a growing recognition of the impact of a multi-dimensional safety culture on patient safety practices within healthcare organizations. Despite this awareness, understanding the relationships among safety culture dimensions and their relative importance in dynamic and complex healthcare delivery remains unclear. To address this gap, we developed a Bayesian Belief Network (BBN) model to quantitatively assess the significance of various safety culture dimensions in influencing patient safety. Utilizing Hospital Survey on Patient Safety Culture (HSOPSC) SOPS Hospital Survey 2.0 data from U.S. teaching hospitals, this study provides insights into the strength of influence among interconnected variables, explores primary factors contributing to the patient safety, and offers guidance for decision-makers on optimal resource utilization by prioritizing safety culture dimensions based on their relative importance. Our findings identify three leading dimensions of safety culture for their significant association with patient safety ratings: 'communication openness,' 'supervisor support for patient safety,' and 'organizational learning - continuous support'. This analysis supports decision-making, providing insights into the relative priority of safety culture dimensions for optimal resource allocation. The BBN is a novel methodology, providing a visual representation of the probabilistic relationships and potential approaches for enhancing patient safety within healthcare settings.

关键词： Safety culture Patient safety Healthcare analytics Bayesian Belief Network SOPS hospital survey 2.0 Machine learning Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Impact assessment of country risk on logistics performance using a Bayesian Belief Network model

引用

KYBERNETES 2023年第5期52卷 1620-1642页

作者： Qazi, Abroon simsekler, mecit can emre Formaneck, Steven Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Canadian Univ Dubai Fac Management Dubai U Arab Emirates

Purpose This paper aims to assess the impact of different drivers of country risk, including business environment, corruption, economic, environmental, financial, health and safety and political risks, on the country-level logistics performance. Design/methodology/approach This study utilizes three datasets published by reputed international organizations, including the World Bank Group, AM Best and Global Risk Profile, to explore interactions among country risk drivers and the Logistics Performance Index (LPI) in a network setting. The LPI, published by the World Bank Group, is a composite measure of the country-level logistics performance. Using the three datasets, a Bayesian Belief Network (BBN) model is developed to investigate the relative importance of country risk drivers that influence logistics performance. Findings The results indicate a moderate to a strong correlation among individual risks and between individual risks and the LPI score. The financial risk significantly varies relative to the extreme states of the LPI score, whereas corruption risk and political risk are the most critical factors influencing the LPI score relative to their resilience and vulnerability potential, respectively. Originality/value This study has made two unique contributions to the literature on logistics performance assessment. First, to the best of the authors' knowledge, this is the first study to establish associations between country risk drivers and country-level logistics performance in a probabilistic network setting. Second, a new BBN-based process has been proposed for logistics performance assessment and operationalized to help researchers and practitioners establish the relative importance of risk drivers influencing logistics performance. The key feature of the proposed process is adapting the BBN methodology to logistics performance assessment through the lens of risk analysis.

关键词： Logistics performance index Financial Economic Political Business environment Health and safety risks Bayesian Belief Network

来源：评论

学校读者我要写书评

暂无评论

Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation

引用

ANNALS OF OPERATIONS RESEARCH 2023年第1期322卷 241-272页

Several techniques have been proposed in supply chain risk management to capture causality among risks in a network setting and prioritize risks regarding their network-wide propagation impact. However, these techniques might be unable to capture the Risk Network Value at Risk (RNVaR), the maximum risk exposure expected at a given confidence level for a given timeframe, associated with individual supply chain performance measures within a network setting. With an exclusive focus on using point estimates in existing techniques, there is a risk of overlooking tail distributions and ignoring critical risks. In this paper, we aim to address this research gap by introducing new risk metrics and a new process theoretically grounded in Bayesian Belief Network and Monte Carlo Simulation frameworks. Integrating these two techniques helps establish the RNVaR that is associated with different performance measures and the relative importance of individual risks for resource allocation. We demonstrate the application of the proposed process through a real case study in the telecommunications industry and compare the results of this study with existing approaches.

关键词： Supply chain risk management Bayesian belief network Monte Carlo simulation Risk network value at risk Risk metrics Performance measures

来源：评论

学校读者我要写书评

暂无评论

A Path Towards Human-AI Decision-Making in Sepsis Care through Human-Centered Systems-Based Design Approach

A Path Towards Human-AI Decision-Making in Sepsis Care throu...

引用

2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024

作者： Rahmadani, Firda simsekler, mecit can emre Khalifa University of Science and Technology Department of Management Science and Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350367355

Sepsis is a critical condition when the body's response to infection triggers widespread inflammation, leading to severe health complications. Despite advancements in medical care through AI-based prediction models and clinical decision support systems, sepsis remains a leading cause of death in hospitals, highlighting the urgent need for improved diagnostic methods, treatment protocols, and public awareness to prevent this severe condition effectively. Further, the ethical implications and risks associated with AI implementation necessitate careful consideration of diversity, equity, and inclusion in designing and deploying human-AI decision support. To consider such challenges, we propose a human-centered systems-based (HCSB) design approach to AI implementation that prioritizes user needs systematically and ensures fair and equitable treatment across diverse patient populations, addressing disparities that may arise from socio-economic factors, geographic location, or cultural differences. Key considerations include the development of inclusive datasets that reflect diverse patient demographics, designing transparent and interpretable algorithms, and establishing protocols for continuous monitoring and evaluation to detect and mitigate biases. Moreover, collaboration among interdisciplinary teams, including healthcare professionals, AI engineers, ethicists, and community representatives, is essential to embedding an HCSB design approach throughout the decision support lifecycle. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Adoption of a Data-Driven Bayesian Belief Network Investigating Organizational Factors that Influence Patient Safety

引用

RISK ANALYSIS 2022年第6期42卷 1277-1293页

作者： simsekler, mecit can emre Qazi, Abroon Khalifa Univ Sci & Technol Dept Ind & Syst Engn POB 127788 Abu Dhabi U Arab Emirates UCL Sch Management London E14 5AA England Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates

Medical errors pose high risks to patients. Several organizational factors may impact the high rate of medical errors in complex and dynamic healthcare systems. However, limited research is available regarding probabilistic interdependencies between the organizational factors and patient safety errors. To explore this, we adopt a data-driven Bayesian Belief Network (BBN) model to represent a class of probabilistic models, using the hospital-level aggregate survey data from U.K. hospitals. Leveraging the use of probabilistic dependence models and visual features in the BBN model, the results shed new light on relationships existing among eight organizational factors and patient safety errors. With the high prediction capability, the data-driven approach results suggest that "health and well-being" and "bullying and harassment in the work environment" are the two leading factors influencing the number of reported errors and near misses affecting patient safety. This study provides significant insights to understand organizational factors' role and their relative importance in supporting decision-making and safety improvements.

关键词： Bayesian Network healthcare operations medical errors patient safety risk

来源：评论

学校读者我要写书评

暂无评论

The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2025年第1期25卷 1-17页

作者： Alkhanbouli, Razan Almadhaani, Hour Matar Abdulla Alhosani, Farah simsekler, mecit can emre Khalifa Univ Sci & Technol Dept Management Sci & Engn Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Dept Biomed Engn & Biotechnol Abu Dhabi U Arab Emirates

Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This study conducts a Systematic Literature Review (SLR) following the PRISMA protocol, synthesizing findings from 30 selected studies to examine XAI's evolving role in disease prediction. It explores commonly used XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), and their impact across medical fields in disease prediction. The review highlights key gaps, including limited dataset diversity, model complexity, and reliance on single data types, emphasizing the need for greater interpretability and data integration. Addressing these issues is crucial for advancing AI in healthcare. This study contributes by outlining current challenges and potential solutions, suggesting directions for future research to develop more reliable and robust XAI methods.

关键词： Explainable artificial intelligence XAI Healthcare AI Machine learning Disease prediction Disease recognition Patient safety Decision support systems Risk management

来源：评论

学校读者我要写书评

暂无评论

A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector

引用

ANNALS OF OPERATIONS RESEARCH 2024年第3期342卷 1797-1817页

作者： Al Nuairi, Arwa simsekler, mecit can emre Qazi, Abroon Sleptchenko, Andrei Khalifa Univ Sci & Technol Dept Ind & Syst Engn POB 127788 Abu Dhabi U Arab Emirates Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates

Patient experience is a key quality indicator driven by various patient- and provider-related factors in healthcare systems. While several studies provided different insights on patient experience factors, limited research investigates the interdependencies between provider-related factors and patient experience. This study aims to develop a data-driven Bayesian belief network (BBN) model that explores the role and relative importance of provider-related factors influencing patient experience. A BBN model was developed using structural learning algorithms such as tree augmented Naive Bayes. We used hospital-level aggregated survey data from the British National Health Service to explore the impact of eight provider-related factors on overall patient experience. Moreover, sensitivity and scenario-based analyses were performed on the model. Our results showed that the most influential factors that lead to a high patient experience score are: (1) confidence and trust, (2) respect for patient-centered values, preferences, and expressed needs, and (3) emotional support. Further sensitivity and scenario analyses provided significant insights into the effect of different hypothetical interventions and how the patient experience is affected. The study findings can help healthcare managers utilize and allocate their resources more effectively to improve the overall patient experience in healthcare systems.

关键词： Patient experience Healthcare operations Machine learning Bayesian belief network model Healthcare analytics Healthcare quality Healthcare systems Data-driven decision making

来源：评论

学校读者我要写书评

暂无评论

Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach

引用

IEEE ACCESS 2024年 12卷 166058-166067页

作者： Toffaha, Khaled M. simsekler, mecit can emre Alshehhi, Aamna Omar, Mohammed Atif Khalifa Univ Dept Management Sci & Engn Abu Dhabi U Arab Emirates Khalifa Univ Dept Biomed Engn & Biotechnol Abu Dhabi U Arab Emirates

Healthcare systems face significant challenges and financial burdens due to patient no-shows, highlighting the need for accurate and interpretable predictive models. This study evaluated the efficacy of twelve classification algorithms that can generate self-explanatory results illustrations across four categories: Rule Set classifiers, Rule List classifiers, Rule Tree classifiers, and Algebraic Models, using a real-world dataset, "Brazilian Medical Appointment No Shows". Analysis across multiple performance metrics revealed significant differences among the algorithms. Advanced models like Tree- Generalized Additive Model (GAM), Fast Interpretable Greedy-Tree Sums (FIGS), Tree Alternating Optimization (TAO) Tree, and RuleFit demonstrated superior predictive capabilities using Over-Sampling and feature selection, achieving an accuracy of 87.53%, AUC 0.87, and F1-score of 0.86, compared to basic tree algorithms like Greedy Tree and C4.5. While Tree-GAM showed high accuracy, it had a significantly longer runtime of approximately 101 seconds. FIGS and TAO Tree offered compelling alternatives with comparable accuracy but significantly reduced computational demands, with runtimes under 1 second. These findings highlight the trade-offs between predictive power, computational efficiency, and practical implementation in healthcare settings. The study also revealed the value of flexible, adaptive architectures in capturing nuanced factors influencing patient no-shows. Overall, these advanced algorithms present accurate and interpretable solutions for forecasting patient no-shows, with FIGS and TAO Tree emerging as particularly effective choices that offer a good balance between predictive insight and practical viability. These insights aim to guide health systems in optimizing patient access and reliability while addressing the complex issue of no-shows, underscoring the importance of considering multiple performance metrics when selecting algorithms for real-world applicati

关键词： Patient no-shows decision tree algorithms greedy tree classifiers additive model trees hierarchical shrinkage trees hierarchical shrinkage trees healthcare analytics healthcare analytics machine learning machine learning hierarchical shrinkage trees healthcare analytics machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：