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DEANN: A healthcare analytic methodology of data envelopment analysis and artificial neural networks for the prediction of organ recipient functional status

Citations

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Cited by:

  1. Sebastian Kohl & Jan Schoenfelder & Andreas Fügener & Jens O. Brunner, 2019. "The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals," Health Care Management Science, Springer, vol. 22(2), pages 245-286, June.
  2. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
  3. Bodin Singpai & Desheng Wu, 2020. "Using a DEA–AutoML Approach to Track SDG Achievements," Sustainability, MDPI, vol. 12(23), pages 1-26, December.
  4. Abdelrahman E. E. Eltoukhy & Ibrahim Abdelfadeel Shaban & Felix T. S. Chan & Mohammad A. M. Abdel-Aal, 2020. "Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations," IJERPH, MDPI, vol. 17(19), pages 1-25, September.
  5. Sinem Savaşer & Ömer Burak Kınay & Bahar Yetis Kara & Pelin Cay, 2019. "Organ transplantation logistics: a case for Turkey," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 327-356, June.
  6. Ali Taghi-Molla & Masoud Rabbani & Mohammad Hosein Karimi Gavareshki & Ehsan Dehghani, 2020. "Safety improvement in a gas refinery based on resilience engineering and macro-ergonomics indicators: a Bayesian network–artificial neural network approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 641-654, June.
  7. Alves, André Bernardo & Wanke, Peter & Antunes, Jorge & Chen, Zhongfei, 2020. "Endogenous network efficiency, macroeconomy, and competition: Evidence from the Portuguese banking industry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  8. Yu Shi & Anyu Yu & Huong Ngo Higgins & Joe Zhu, 2021. "Shared and unsplittable performance links in network DEA," Annals of Operations Research, Springer, vol. 303(1), pages 507-528, August.
  9. Francisco Javier Santos Arteaga & Debora Di Caprio & David Cucchiari & Josep M Campistol & Federico Oppenheimer & Fritz Diekmann & Ignacio Revuelta, 2021. "Modeling patients as decision making units: evaluating the efficiency of kidney transplantation through data envelopment analysis," Health Care Management Science, Springer, vol. 24(1), pages 55-71, March.
  10. Eltoukhy, Abdelrahman E.E. & Wang, Z.X. & Chan, Felix T.S. & Fu, X., 2019. "Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 143-168.
  11. Sabri Boubaker & T.D.Q. Le & T. Ngo & R. Manita, 2023. "Predicting the Performance of MSMEs: A Hybrid DEA-machine Learning Approach," Post-Print hal-04434027, HAL.
  12. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP022020, School of Economics, University of Queensland, Australia.
  13. Jéfferson Colombo & Peter Wanke & Jorge Antunes & Abul Kalam Azad, 2022. "Unveiling endogeneity between competition and efficiency in European banks: a robust econometric-neural network approach," SN Business & Economics, Springer, vol. 2(3), pages 1-46, March.
  14. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
  15. Yong Tan & Peter Wanke & Jorge Antunes & Ali Emrouznejad, 2021. "Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis," Annals of Operations Research, Springer, vol. 306(1), pages 131-171, November.
  16. Al-Ebbini, Lina & Oztekin, Asil & Chen, Yao, 2016. "FLAS: Fuzzy lung allocation system for US-based transplantations," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1051-1065.
  17. Wang, Fan & Zhang, Shengfan & Henderson, Louise M., 2018. "Adaptive decision-making of breast cancer mammography screening: A heuristic-based regression model," Omega, Elsevier, vol. 76(C), pages 70-84.
  18. Alexandre Marinho & Claudia Affonso Silva Araújo, 2021. "Using data envelopment analysis and the bootstrap method to evaluate organ transplantation efficiency in Brazil," Health Care Management Science, Springer, vol. 24(3), pages 569-581, September.
  19. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
  20. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
  21. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
  22. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
  23. Azadi, Majid & Yousefi, Saeed & Farzipoor Saen, Reza & Shabanpour, Hadi & Jabeen, Fauzia, 2023. "Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis," Journal of Business Research, Elsevier, vol. 154(C).
  24. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
  25. Valero-Carreras, Daniel & Aparicio, Juan & Guerrero, Nadia M., 2021. "Support vector frontiers: A new approach for estimating production functions through support vector machines," Omega, Elsevier, vol. 104(C).
  26. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).
  27. Jorge Antunes & Abdollah Hadi-Vencheh & Ali Jamshidi & Yong Tan & Peter Wanke, 2022. "Bank efficiency estimation in China: DEA-RENNA approach," Annals of Operations Research, Springer, vol. 315(2), pages 1373-1398, August.
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