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

作者： Toffaha, Khaled simsekler, mecit can emre Omar, Mohammed Management Science and Engineering Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350367355

Patient no-shows present significant challenges and costs to healthcare systems, prompting a focused exploration of hospital outpatient no-show prediction using explainable machine learning (ML) in this review. The systematic methodology aligns with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring transparency, replicability, and thoroughness. A comprehensive search across major literature databases yielded 92 potential studies on ML models for outpatient no-show prediction. Following screening and eligibility assessments, 51 studies met inclusion criteria, emphasizing outpatient no-shows, leveraging explainable ML, featuring predictive modeling, and reporting quantitative metrics. Examining the publication distribution from 2010 to 2024 reveals a discernible upward linear trend, indicating the growing relevance of this research domain. The preference for more interpretable ML models over time, such as regression, decision trees, and ensemble methods, underscores their transparency and actionability. Despite challenges like limited model generalizability and clinician hesitation, proposed solutions, including collaborative learning and feature engineering pipelines, aim to enhance the reliability and applicability of outpatient noshow prediction models. This comprehensive strategy addresses existing challenges, facilitating the effective integration of explainable ML in healthcare settings and contributing to improved outpatient appointment attendance. In conclusion, this systematic review provides a robust foundation for understanding and implementing explainable ML approaches in predicting hospital outpatient no-shows, offering valuable insights for healthcare practitioners and researchers alike. © 2024 IEEE.

关键词： Prediction models

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Exploring Drivers of Staff Engagement in Healthcare Organizations Using Tree-Based Machine Learning Algorithms

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2023年第8期70卷 2988-2997页

作者： Al-Nammari, Ragheb simsekler, mecit can emre Gabor, Adriana Felicia Qazi, Abroon Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi 127788 U Arab Emirates Khalifa Univ Sci & Technol Dept Appl Math Abu Dhabi 127788 U Arab Emirates Amer Univ Sharjah Dept Mkt & Informat Syst Sharjah 26666 U Arab Emirates

Staff engagement in the work environment is vital to organizational success. Engaged staff are motivated and indulged in their work as they have a sense of belonging, commitment, and loyalty toward their employer, which eventually leads to better performance and outcomes. While various organizational factors are related to staff engagement, limited research is available regarding what drives staff engagement and the degree of their importance in healthcare. Leveraging data-driven approaches, in this article, we employ three machine learning algorithms, random forests, gradient boosting, and extra trees, to identify the relative importance of organizational factors affecting staff engagement. We use hospital-level aggregate survey data from hospitals in the U.K. While staff engagement is the outcome variable, the following factors are used as organizational factors in the prediction model and feature importance analysis: equality, diversity, and inclusion, safety culture, health and wellbeing, immediate managers, quality of appraisals, quality of care, bullying and harassment, violence, and team working. All the algorithms provide comparable prediction results with similar feature importance ranking with respect to prediction accuracy. The results suggest that safety culture is the most influential factor related to staff engagement, followed by the team working. Healthcare managers and decision makers can benefit from this data-driven application to make informed decisions in resource allocation and prioritization efforts to improve staff engagement.

关键词： Data analytics employee engagement gradient boosting (GB) healthcare management machine learning (ML) organizational culture random forest (RF) safety culture staff engagement

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Prioritizing Multidimensional Interdependent Factors Influencing COVID-19 Risk

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RISK ANALYSIS 2022年第1期42卷 143-161页

作者： Qazi, Abroon simsekler, mecit can emre Gaudenzi, Barbara Amer Univ Sharjah Sch Business Adm POB 26666 Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Ind & Syst Engn Abu Dhabi U Arab Emirates Univ Verona Dept Business Adm Verona Italy

COVID-19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy-makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country-level data-set related to humanitarian crises and disasters to explore critical factors influencing COVID-19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID-19 risk in a probabilistic network setting. This study provides unique insights to policy-makers regarding the identification of critical factors influencing COVID-19 risk and their relative importance in a network setting.

关键词： Bayesian Belief Network COVID-19 risk hazard and exposure pandemics vulnerability

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Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第1期24卷 337页

作者： Al-Absi, Dima Tareq simsekler, mecit can emre Omar, Mohammed Atif Anwar, Siddiq Khalifa Univ Sci & Technol Dept Management Sci & Engn POB 127788 Abu Dhabi U Arab Emirates Sheikh Shakhbout Med City Dept Med Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Coll Med & Hlth Sci Med Abu Dhabi U Arab Emirates

This study reviews the studies utilizing Artificial Intelligence (AI) and AI-driven tools and methods in managing Acute Kidney Injury (AKI). It categorizes the studies according to medical specialties, analyses the gaps in the existing research, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the three most common databases (PubMed, Scopus, and EBSCO), which resulted in 27 eligible studies, published between 2012 and 2023. The study showed significant heterogeneity in the design of the models, with variations in clinical settings, patient characteristics, cohort regions, and statistical methods. Most models were developed for AKI in hospitalized patients, particularly those undergoing surgery or in intensive care units. Compact models with a subset of significant predictors were deemed more clinically applicable than full models with all predictors. The findings suggest that AI tools, such as machine learning (ML) algorithms, have high prediction capabilities despite the dynamic and complex association among the influencing factors and AKI. Based on these findings and the recognized need for broader inclusivity, future studies should consider adopting a more inclusive approach by incorporating diverse healthcare settings, including resource-limited or developing countries. This inclusivity will lead to a more holistic understanding of AKI management challenges and facilitate the development of adaptable and universally applicable AI-driven solutions. Additionally, further investigations should focus on refining AI models to enhance their accuracy and interpretability, promoting seamless integration and implementation of AI-based tools in real-world clinical practice. Addressing these key aspects will elevate the effectiveness and impact of AI-driven approaches in managing AKI.

关键词： Acute Kidney Injury AKI Patient safety Artificial Intelligence Healthcare operations Healthcare analytics Machine learning

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Bayesian network and structural equation modeling of dependencies between country-level sustainability risks and logistics performance

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ANNALS OF OPERATIONS RESEARCH 2024年 1-28页

作者： Qazi, Abroon simsekler, mecit can emre Al-Mhdawi, M. K. S. Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Khalifa Univ Sci & Technol Dept Management Sci & Engn Abu Dhabi U Arab Emirates Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough England

The 17 Sustainable Development Goals (SDGs), introduced by the United Nations in 2015, provide a framework for individual countries to track their performance on social, economic, and environmental dimensions of sustainable development. Logistics is considered the backbone of an economy that can influence the development and growth of a country. Using a dataset of 157 countries, this study aims to explore dependencies between the 17 SDG-related risks and logistics performance in a probabilistic network setting. A hybrid methodology, combining Bayesian Belief Networks and Structural Equation Modeling, is used to develop and quantify a network structure. Logistics performance is significantly influenced by four SDGs, namely 'industry, innovation and infrastructure', 'climate action', 'partnerships for the goals', and 'gender equality'. On the other hand, another cluster of SDGs is observed surrounding the 'no poverty' goal, including 'industry, innovation and infrastructure', 'decent work and economic growth', 'reduced inequality', 'zero hunger', 'sustainable cities and communities', 'affordable and clean energy', and 'clean water and sanitation'. This paper proposes and operationalizes a new hybrid methodology to explore dependencies between the SDGs and logistics performance, thereby providing valuable insights to policymakers and researchers about the association between sustainable development and logistics performance.

关键词： Bayesian belief networks Logistics Structural equation modeling Sustainability risks Sustainable development goals

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Prioritizing interdependent drivers of financial, economic, and political risks using a data-driven probabilistic approach (Feb, 10.1057/s41283-022-00089-8, 2022)

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RISK MANAGEMENT-AN INTERNATIONAL JOURNAL 2022年第2期24卷 186-186页

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

Financial, economic, and political risks pose a significant threat to the development and progress of countries as such risks can impact all spheres of life including education, healthcare, logistics, transportation, and safety among others. Although these risks seem quite distinct, they are mutually influenced by multidimensional interdependent factors such as internal and external conflict, socioeconomic conditions, corruption, law and order, and bureaucratic quality among others. In this paper, we utilize a data-driven approach to explore dependencies among factors influencing financial, economic, and political risks and establish their relative importance in a network setting while capturing the entire distribution of individual factors. A probabilistic network-based model was developed using the data by the International Country Risk Guide, which revealed significant differences between the conventional and the proposed schemes for prioritizing drivers of political, economic, and financial risks. Internal conflict and socioeconomic conditions were considered as the most critical factors in terms of reducing and enhancing the network-wide risk exposure, respectively. The two prioritization schemes relative to the vulnerability and resilience impact of individual factors are not correlated and therefore, policy-makers need to focus on both schemes while developing risk mitigation strategies.

关键词： Bayesian belief network Conflict Corruption Data-driven approach Financial risks International country risk guide Socioeconomic conditions

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Worst Expected Best method for assessment of probabilistic network expected value at risk: application in supply chain risk management

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2022年第1期39卷 155-175页

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

Purpose The purpose of this paper is to develop and operationalize a process for prioritizing supply chain risks that is capable of capturing the value at risk (VaR), the maximum loss expected at a given confidence level for a specified timeframe associated with risks within a network setting. Design/methodology/approach The proposed "Worst Expected Best" method is theoretically grounded in the framework of Bayesian Belief Networks (BBNs), which is considered an effective technique for modeling interdependency across uncertain variables. An algorithm is developed to operationalize the proposed method, which is demonstrated using a simulation model. Findings Point estimate-based methods used for aggregating the network expected loss for a given supply chain risk network are unable to project the realistic risk exposure associated with a supply chain. The proposed method helps in establishing the expected network-wide loss for a given confidence level. The vulnerability and resilience-based risk prioritization schemes for the model considered in this paper have a very weak correlation. Originality/value This paper introduces a new "Worst Expected Best" method to the literature on supply chain risk management that helps in assessing the probabilistic network expected VaR for a given supply chain risk network. Further, new risk metrics are proposed to prioritize risks relative to a specific VaR that reflects the decision-maker's risk appetite.

关键词： Bayesian belief networks Risk appetite Risk metrics Supply chain risks Value at risk Worst Expected Best method

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Nurse Rostering via Mixed-Integer Programming 10

Nurse Rostering via Mixed-Integer Programming

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10th International Conference on Industrial Engineering and Applications, ICIEA 2023

作者： Ouda, Eman Sleptchenko, Andrei simsekler, mecit can emre Department of Industrial and Systems Engineering Khalifa University Abu Dhabi United Arab Emirates Research Center on Digital Supply Chain and Operations Management Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781643684086

Nurse scheduling is a topic widely studied due to its overall effect on patient care and hospital performance. This research focuses on formulating a Mixed Integer Programming (MIP) workforce scheduling model as a nurse rostering problem. The model incorporates multiple objectives of individual nurse preference and qualification. This is approached by categorizing nurses into different hierarchical levels based on their qualifications and positions;Head Nurse, Senior Nurse, Nurse, and Assistant Nurse. Moreover, each nurse's holiday preferences are accounted for in the model. The proposed MIP model is solved to generate a schedule to meet hospital demand and individual nurse preferences. A MIP solver using Python 3 was used to find the optimal solution by cutting planes. The sensitivity analysis and computational results reflect different scenarios and scheduling to fit all hospital environments and demands. © 2023 The authors and IOS Press.

关键词： Python

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STREAMLINING OUTPATIENT CLINIC PERFORMANCE: SIMULATION MODELING FOR INCOMPLETE DATA HANDLING 50

STREAMLINING OUTPATIENT CLINIC PERFORMANCE: SIMULATION MODEL...

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50th International Conference on Computers and Industrial Engineering: Sustainable Digital Transformation, CIE 2023

作者： Alqaryuti, Alaa emre simsekler, mecit can Sleptchenko, Andrei An, Heungjo Department of Industrial and Systems Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Department of Industrial Engineering Kumoh National Institute of Technology Korea Republic of

ISBN: (纸本)9781713886952

The study focuses on addressing the challenge of handling missing or incomplete data points in complex and dynamic healthcare systems during data collection. Its objective is to tackle the issue of processing missing data in real-life system simulation and propose improvement conditions that would optimize and reduce the patients’ waiting time. The study develops a customized model based on hospital requirements and effectively tackles the issue of incomplete data using OptQuest, which evaluates statistical outputs from the simulation and identifies optimal parameters for model distribution. To validate and verify the model, historical data from an outpatient clinic are used, specifically aiming to reduce patient waiting time. A comparison is made between the optimized simulation model and the existing data for verification purposes. The results obtained by simulation modeling demonstrate significant improvements in all key performance indicators compared to the existing system analysis, highlighting potential reductions in waiting time of patients. © 2023 Computers and Industrial Engineering. All rights reserved.

关键词： Data handling

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Exploring temporal dependencies among country-level logistics performance indicators

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BENCHMARKING-AN INTERNATIONAL JOURNAL 2025年第5期32卷 1825-1856页

作者： Qazi, Abroon Al-Mhdawi, M. K. S. simsekler, mecit can emre Amer Univ Sharjah Sch Business Adm Sharjah U Arab Emirates Teesside Univ Middlesbrough Cleveland England Khalifa Univ Abu Dhabi U Arab Emirates

PurposeThe Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank's LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).Design/methodology/approachA Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank's LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 *** prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that "logistics competence and quality" is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that "customs" is the most critical indicator for improving the performance on the "international shipments" indicator, whereas "logistics competence and quality" can significantly improve the performance on the "timeliness" and "tracking and tracing" indicators. The sensitivity analysis of the model reveals that "logistics competence and quality" and "infrastructure" are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the

关键词： Bayesian belief network Infrastructure Logistics competence Logistics performance index Temporal modeling Timeliness

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