Research Interests

  • Machine Learning

  • Deep Learning

  • Predictive Analytics

  • Health Analytics

  • Privacy in Social Media

  • Technology Addiction


  • 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 (forthcoming)

  • Davazdahemami, B., Zolbanin, HM., Delen, D., An explanatory analytics for early detection of chronic risk factors in pandemics (2022). Healthcare Analytics, 100020.

  • Davazdahemami, B., Peng, P., Delen, D., A deep learning approach for predicting early bounce-backs to the emergency departments (2022). Healthcare Analytics, 100018.

  • 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

  • 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) .

  • 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) .

  • 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: )

  • 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: )

  • 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: )

  • 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.

Manuscripts Under Review

  • Davazdahemami, B., Peng, P., Delen, D. Predicting Early Bounce-backs to the Emergency Department: A Deep Learning Approach. ACM Transactions on Management Information Systems (under review)

  • Zolbanin, H., Davazdahemami, B., H. Zadeh, A. Miscommunication in the Age of Communication: A Crowdsourcing Framework for Symptom Surveillance at the Time of Pandemics. Decision Support Systems (Under Review)

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

Ongoing Research Projects

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

  • Delen. D., Davazdahemami, B. An Introduction to Deep Learning (Book Chapter for the new edition of “Business Intelligence, Analytics, and Data Science: A Managerial Perspective” book by Sharda, Delen, and Turban; Pearson; ISBN: 0134633288).

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