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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. Aleksandar Kemiveš & Lidija Barjaktarović & Milan Ranđelović & Milan Čabarkapa & Dragan Ranđelović, 2024. "Assessing the Efficiency of Foreign Investment in a Certification Procedure Using an Ensemble Machine Learning Model," Mathematics, MDPI, vol. 12(7), pages 1-26, March.
  4. 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.
  5. 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).
  6. Yousefi, Saeed & Saen, Reza Farzipoor & Shabanpour, Hadi & Ghods, Kian, 2024. "An innovative patient clustering method using data envelopment Analysis–Discriminant analysis and artificial neural networks: A case study in healthcare systems," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
  13. Minh-Hieu Le & Wen-Min Lu, 2024. "An integrated multiple objective decision making approach for exploring the competitiveness of pharmaceutical multinational enterprises," Annals of Operations Research, Springer, vol. 341(1), pages 401-426, October.
  14. 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.
  15. 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).
  16. 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.
  17. 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).
  18. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP092020, School of Economics, University of Queensland, Australia.
  19. Bodin Singpai & Desheng Wu, 2020. "Using a DEA–AutoML Approach to Track SDG Achievements," Sustainability, MDPI, vol. 12(23), pages 1-26, December.
  20. 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.
  21. 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.
  22. 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.
  23. Sunil Kumar Jauhar & Praveen Vijaya Raj Pushpa Raj & Sachin Kamble & Saurabh Pratap & Shivam Gupta & Amine Belhadi, 2024. "A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries," Annals of Operations Research, Springer, vol. 332(1), pages 405-431, January.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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).
  31. 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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