Machine Learning

Deep Learning

Explainable AI

Predictive Analytics

Health Analytics

Privacy in Social Media

Technology Addiction

Topuz, K., Davazdahemami, B., Delen, D. A Bayesian Belief Network-based Analytics Methodology for Early-Stage Risk Detection of Novel Diseases. Annals of Operations Research (http://dx.doi.org/10.1007/s10479-023-05377-4)

Delen, D., Davazdahemami, B., Rasouli, E. Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework. Information System Frontiers (2023) (https://link.springer.com/article/10.1007/s10796-023-10397-3)

Davazdahemami, B., Kalgotra, P., Zolbanin, H., Delen, D. A Developer-Oriented Recommender Model for the App Store: A Predictive Network Analytics Approach (2023). Journal of Business Research (https://doi.org/10.1016/j.jbusres.2023.113649)

Rasouli, E., Delen, D., Zhao, H., Davazdahemami, B. A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement (2022). Journal of Healthcare Informatics Research (forthcoming)

Davazdahemami, B., Zolbanin, H., Delen, D. Deep Learning with Minimal Coding Effort: A Tutorial on Theory and Implementation of Deep Artificial Neural Networks. PROCEEDINGS OF THE 2021 PRE-ICIS SIGDSA SYMPOSIUM (https://aisel.aisnet.org/sigdsa2021/5/) 

Davazdahemami, B., Zolbanin, HM., Delen, D., An Explanatory Machine Learning Framework for Studying Pandemics: The Case of COVID-19 Emergency Department Readmissions (2022). Decision Support Systems 

Davazdahemami, B., Zolbanin, HM., Delen, D., An explanatory analytics for early detection of chronic risk factors in pandemics (2022). Healthcare Analytics, 100020. https://bit.ly/3nnlOHf 

Davazdahemami, B., Peng, P., Delen, D., A deep learning approach for predicting early bounce-backs to the emergency departments (2022). Healthcare Analytics, 100018. https://bit.ly/3GB19He 

Zolbanin, H.M., Hassan Zadeh, A., Davazdahemami, B. Miscommunication in the Age of Communication: A Crowdsourcing Framework for Symptom Surveillance at the Time of Pandemics (2021). International journal of medical informatics, 151, 104486.

Eryarsoy, E., Delen, D., Davazdahemami, B.., Topuz, K. A Novel Diffusion-Based Approach to Estimating Cases, Hospitalizations, and Fatalities in Epidemics: The Case of COVID-19 (2021). Journal of Business Research 124, 163-178 https://bit.ly/3lZUjjr 

Eryarsoy, E., Davazdahemami, B., Delen, D., Adjusting COVID-19 Reports for Age Disparities: A Framework for Transferring Knowledge between Affected Countries and Comparing their Reporting Performances (2020) . https://www.medrxiv.org/content/10.1101/2020.08.31.20185223v1 

Davazdahemami, Hammer, B., B., Kalgotra, P.,  Luse, A., From General to Situational Privacy Concerns: A New Mechanism to Explain Information Disclosure in Social Networks (2020).  Communications of Association of Information Systems 47(1) . https://doi.org/10.17705/1CAIS.04730 

Delen, D., Eryarsoy, E., Davazdahemami, B. (2020). No Place like Home: Cross-National Data Analysis of the Efficacy of Social Distancing during the COVID-19 Pandemic. JMIR Public Health and Surveillance 2020;6(2):e19862 (Full text available at: https://doi.org/10.2196/19862 ) 

Zolbanin, H. M., Davazdahemami, B., Delen, D., & Zadeh, A. H. (2020). Data analytics for the sustainable use of resources in hospitals: Predicting the length of stay for patients with chronic diseases. Information & Management, 103282. 

Delen, D., Davazdahemami, B., Eryarsoy, E., Tomak, L., Valluru, A. (2020) Using Predictive Analytics to Identify Drug-Resistant Epilepsy Patients. Health Informatics Journal 26 (1), 449-460. 

Davazdahemami, B., Delen, D. (2019). Examining the Effect of Prescriptions Sequence on Developing Adverse Drug Reactions; the Case of Renal Failure in Diabetic Patients. International Journal of Medical Informatics, 125, 62-70.

Davazdahemami, B., & Delen, D. (2019). The confounding role of common diabetes medications in developing acute renal failure: A data mining approach with emphasis on drug-drug interactions. Expert Systems with Applications, 123, 168-177. 

Davazdahemami, B., Hammer, B., Luse, A., Kalgotra, P. (2018) The Role of Parallelism in Resolving the Privacy Paradox of Information Disclosure in Social Networks. Proceedings of thirty ninth International Conference on Information Systems (ICIS) 2018. (Available at: https://aisel.aisnet.org/icis2018/security/Presentations/5/ )

Davazdahemami, B., Delen, D. (2018) A Chronological Pharmacovigilance Network Analytics Approach for Predicting Adverse Drug Events. Journal of the American Medical Informatics Association (JAMIA), Volume 25, Issue 10, 1 October 2018, Pages 1311–1321. (Available online: https://goo.gl/7FE7nR )

Davazdahemami, B., Luse, A., Scheibe, K., Townsend, A. (2018) Training, Self-Efficacy, and Performance; a Replication Study. AIS Transactions on Replication Research 4, no. 1 (2018): 3.

Davazdahemami, B., Hammer, B., & Soror, A. (2016, January). Addiction to mobile phone or addiction through mobile phone? In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 1467-1476). IEEE.

Azadeh, A., Davazdahemami, B. (2012) An Integrated Artificial Neural Network Approach for Predicting Internet Penetration Rate. 25th European Conference of Operational Research, 2012- Vilnius, Lithuania.

Rabbani, M., Davazdahemami, B., Manavizadeh, N. (2010) A Mathematical Model and Two Genetic Algorithms for Balancing Mixed-Model Two-Sided Assembly Lines with Multifunctional Workers Assignment. Proceedings of 7th International Industrial Engineering Conference 2010, Isfahan- Iran.

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Davazdahemami, B., Delen, D. A Deep Learning Approach for Extracting Relations between Mentions of Drugs and Events in the Social Media Text. Target: JAMIA.

Davazdahemami, B., Zolbanin, H., Delen, D. An Optimized Machine Learning Approach to Predict Early Readmission of COVID-19 Patients.