This page lists my peer-reviewed journal articles, conference proceedings, and other scholarly work. My research is focused on creating and applying predictive models and decision support systems to solve complex problems in risk analysis, healthcare, and business.

My primary research interests include:

• Bayesian Belief Networks

• Prediction Models & Machine Learning

• Risk Analysis & Decision Support Systems

• Health Analytics & Informatics

Citation Metrics Summary

This summary is based on my Google Scholar profile. For the most current data, please visit my Google Scholar Profile. (Note: You will need to replace the link with your actual Google Scholar profile URL).

Citations

Since 2025: 950+ 

h-index: 14+

i10-index: 18+

• h-index: A measure of productivity and citation impact, indicating I have published 14 papers that have each been cited at least 14 times.

• i10-index: The number of publications with at least 10 citations.

List of Publications

Publications are listed in reverse chronological order.

2025

• K Topuz, A Bajaj, K Coussement, TL Urban. (2025). Interpretable machine learning and explainable artificial intelligence. Annals of Operations Research, 347(2), 775-782. [Cited by: 1]

• S Sahbaz, K Topuz, SJ Schwartz, P Montero-Zamora. (2025). Understanding cultural stress and mental health among Latinos in the us: probabilistic omnidirectional inference model. Annals of Operations Research, 1-21. [Cited by: 1]

• I Ghosh, AV Megaravalli, MZ Abedin, K Topuz. (2025). Prediction and decoding of metaverse coin dynamics: a granular quest using MODWT-Facebook’s prophet-TBATS and XAI methodology. Annals of Operations Research, 1-37. [Cited by: 1]

• S Tutun, K Topuz, A Tosyali, A Bhattacherjee, G Li. (2025). Explainable artificial intelligence for mental disorder screening: A computational design science approach. Journal of Management Information Systems, 41(4), 958-981. [Cited by: 8]

2024

• K Topuz, TL Urban, RA Russell, MB Yildirim. (2024). Decision support system for appointment scheduling and overbooking under patient no-show behavior. Annals of Operations Research, 342(1), 845-873. [Cited by: 14]

• K Topuz, B Davazdahemami, D Delen. (2024). A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases. Annals of Operations Research, 341(1), 673-697. [Cited by: 17]

• A Ahmed, K Topuz, M Moqbel, I Abdulrashid. (2024). What makes accidents severe! explainable analytics framework with parameter optimization. European Journal of Operational Research, 317(2), 425-436. [Cited by: 17]

• K Topuz, TL Urban, MB Yildirim. (2024). A Markovian score model for evaluating provider performance for continuity of care—An explainable analytics approach. European Journal of Operational Research, 317(2), 341-351. [Cited by: 10]

• K Coussement, MZ Abedin, M Kraus, S Maldonado, K Topuz. (2024). Explainable AI for enhanced decision-making. Decision Support Systems, 114276. [Cited by: 19]

• BD Jones, K Topuz, S Sahbaz. (2024). Predicting undergraduate student evaluations of teaching using probabilistic machine learning: The importance of motivational climate. Studies in Educational Evaluation, 81, 101353. [Cited by: 3]

• E Eryarsoy, K Topuz, C Demiroglu. (2024). Disentangling human trafficking types and the identification of pathways to forced labor and sex: an explainable analytics approach. Annals of Operations Research, 335(2), 761-795. [Cited by: 11]

2023

• I Abdulrashid, H Friji, K Topuz, H Ghazzai, D Delen, Y Massoud. (2023). An analytical approach to evaluate the impact of age demographics in a pandemic. Stochastic Environmental Research and Risk Assessment, 37(10), 3691-3705. [Cited by: 5]

• B Cankaya, K Topuz, D Delen, A Glassman. (2023). Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents. Omega, 120, 102906. [Cited by: 24]

• S Tutun, G Li, A Bhattacherjee, K Topuz, A Tosyali. (2023). MDScan Explainable AI Artifact for Screening and Monitoring Mental Disorders. IISE Annual Conference and Expo.

• K Topuz, B Cankaya, A Glassman. (2023). Business Inferences and Risk Modeling with Machine Learning; The case of Aviation Incidents. Proceedings of the 56th Hawaii International Conference on System Sciences, 1238. [Cited by: 8]

• K Topuz, A Bajaj, I Abdulrashid. (2023). Introduction to the Minitrack on Interpretable Machine Learning. Proceedings of the 56th Hawaii International Conference on System Sciences, 1236.

• S Tutun, A Bhattacherjee, K Topuz, A Tosyali, G Li. (2023). MDSCAN: AN EXPLAINABLE ARTIFICIAL INTELLIGENCE ARTIFACT FOR MENTAL HEALTH SCREENING.

2021

• K Topuz, D Delen. (2021). A probabilistic Bayesian inference model to investigate injury severity in automobile crashes. Decision Support Systems, 150, 113557. [Cited by: 39]

• K Topuz, BD Jones, S Sahbaz, M Moqbel. (2021). Methodology to combine theoretical knowledge with a data-driven probabilistic graphical model. Journal of Business Analytics, 4(2), 125-139. [Cited by: 10]

• E Eryarsoy, D Delen, B Davazdahemami, K Topuz. (2021). A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19. Journal of Business Research, 124, 163-178. [Cited by: 31]

2020

• FD Zengul, T Lee, D Delen, A Almehmi, NV Ivankova, T Mehta, K Topuz. (2020). Research themes and trends in ten top-ranked nephrology journals: A text mining analysis. American Journal of Nephrology, 51(2), 147-159. [Cited by: 12]

• M Moqbel, VL Bartelt, K Topuz, KL Gehrt. (2020). Enterprise social media: combating turnover in businesses. Internet Research, 30(2), 591-610. [Cited by: 41]

• D Delen, K Topuz, E Eryarsoy. (2020). Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition. European Journal of Operational Research, 281(3), 575-587. [Cited by: 94]

2019

• BD Rouyendegh, K Topuz, A Dag, A Oztekin. (2019). An AHP-IFT integrated model for performance evaluation of E-commerce web sites. Information Systems Frontiers, 21, 1345-1355. [Cited by: 55]

2018

• K Topuz, H Uner, A Oztekin, MB Yildirim. (2018). Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network. Annals of Operations Research, 263, 479-499. [Cited by: 90]

• K Topuz, FD Zengul, A Dag, A Almehmi, MB Yildirim. (2018). Predicting graft survival among kidney transplant recipients: A Bayesian decision support model. Decision Support Systems, 106, 97-109. [Cited by: 169]

2017 & Earlier

• D Delen, L Tomak, K Topuz, E Eryarsoy. (2017). Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. Journal of Transport & Health, 4, 118-131. [Cited by: 151]

• A Dag, K Topuz, A Oztekin, S Bulur, FM Megahed. (2016). A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival. Decision Support Systems, 86, 1-12. [Cited by: 97]

• JT Luxhøj, K Topuz. (2013). Probabilistic causal modeling of runway incursion (RI) safety risk. Proceedings of the IIE Annual Conference. [Cited by: 1]

• JT Luxhoj, K Topuz. (2012). Sensitivity Analyses of Risk Factors and Mitigation Effects on a Wake Vortex Encounter Flight Scenario. Industrial and Systems Engineering Research Conference. [Cited by: 3]