| Chronic disease progression prediction: Leveraging case‐based reasoning and big data analytics Z Nenova, J Shang Production and Operations Management 31 (1), 259-280, 2022 | 38 | 2022 |
| Determining an optimal hierarchical forecasting model based on the characteristics of the data set ZD Nenova, JH May Journal of Operations Management 44, 62-68, 2016 | 31 | 2016 |
| Machine learning in healthcare: Operational and financial impact D Anderson, MV Bjarnadottir, Z Nenova Innovative Technology at the Interface of Finance and Operations: Volume I …, 2022 | 18 | 2022 |
| Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data Z Nenova, J Shang Production and Operations Management 31 (2), 583-606, 2022 | 6 | 2022 |
| Appointment utilization as a trigger for palliative care introduction: A retrospective cohort study Z Nenova, J Hotchkiss Jr Palliative medicine 33 (4), 457-461, 2019 | 3 | 2019 |
| Identifying influential individuals and predicting future demand of chronic kidney disease patients ZD Nenova, VL Bartelt Decision Sciences, 2024 | | 2024 |
| How Collaborators Manage Information Sharing and Protection in Flash Collaborations V Bartelt, Z Nenova, M Marabelli Academy of Management Proceedings 2020 (1), 14204, 2020 | | 2020 |
| Essays on the management of appointments for chronic conditions ZD Nenova University of Pittsburgh, 2017 | | 2017 |
| Approximate Dynamic Programming for a Dynamic Appointment Scheduling Problem Z Nenova, M Laguna, D Zhang | | |