How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

Yousefi, Saeed; Shabanpour, Hadi; Ghods, Kian; Saen, Reza Farzipoor

doi:10.1016/j.cie.2022.108933

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…Zhang et al, 2018) (Dawson & Wilby, 1998). By incorporating neural networks into the stochastic DEA framework, we can improve the prediction accuracy of input-output data, especially for large datasets (Yousefi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Data driven approach for stochastic DEA in machine learning and artificial intelligence to improve the accuracy, stability, and interpretability of the model

Sihotang,

Huang,

Alesha

2023

Idea

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The novelty of this research lies in the integration of machine learning and artificial intelligence techniques into stochastic DEA models. While traditional DEA models have been widely used to measure the efficiency of decision-making units, they may not be able to capture complex and nonlinear relationships between inputs and outputs. By integrating advanced machine learning and AI techniques, this research aims to improve the accuracy, stability, and interpretability of stochastic DEA models, providing decision-makers with more reliable and actionable insights. Moreover, this research explores several novel approaches, including the integration of deep learning techniques, ensemble learning, dynamic stochastic DEA models, and explainable AI, to improve the performance of stochastic DEA models. These approaches have the potential to enhance the accuracy of efficiency scores, increase the stability of the model, provide more actionable insights, and improve the model's interpretability. By integrating these approaches into stochastic DEA models, this research aims to provide a comprehensive and effective solution to the problem of measuring the efficiency of decision-making units. This approach has not been explored extensively in the literature, and thus represents a novel and innovative approach to addressing this important research problem.

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Section: Introductionmentioning

confidence: 99%

Data driven approach for stochastic DEA in machine learning and artificial intelligence to improve the accuracy, stability, and interpretability of the model

Sihotang,

Huang,

Alesha

2023

Idea

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“…DEA has a wide application range and involves relatively simple principles. The DEA method can be used to intuitively analyze the efficiency of medical and health resource allocation (10,11).…”

mentioning

confidence: 99%

The Efficiency of Inter-provincial Health Resource Allocation and its Influencing Factors in China---An Empirical Study Based on DEA Method and a Spatial Econometric Model

Meng,

Pei,

Lin

et al. 2023

Preprint

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Background: The Chinese government has been adapting to rapid changes in the domestic and international situation in order to formulate appropriate health policies. However, under the socioeconomic impact of the COVID-19 pandemic, China’s medical and health resource allocation still has problems in terms of insufficiency and uneven distribution. Therefore, how to scientifically allocate and efficiently use medical and health resources has become an urgent issue. Methods: The super-efficiency slack-based measure model and the global Malmquist model were used to measure the efficiency of medical and health resource allocation in 31 provinces in China from 2008 to 2020. Moran’s I was used to test the spatial correlation of the efficiency. A spatial Dubin model was constructed to analyze influencing factors. All data were collected from the China Health Statistics Yearbook and China Statistics Yearbook from 2008 to 2020. Results: The efficiency of medical and health resource allocation calculated by the super-efficiency slack-based measure model showed that the average score was 0.632. The average Malmquist productivity index of the efficiency of medical and health resource allocation of 31 provinces in China from 2008 to 2020 was 1.0897, which was generally positive. The Moran’s I test results showed that the efficiency had a significant spatial positive correlation in the spatial distribution. The regression results of the spatial Dubin model showed that the efficiency was affected by the dependency ratio, the illiteracy rate, the per capita disposable income, the per capita public health budget expenditure, the number of medical insurance participants, and the balance of medical insurance fund revenue and expenditure. Discussion: In view of the problems revealed in the efficiency of medical and health resource allocation from 2008 to 2020, Chinese provinces should fully consider the impacts of the dependency ratio, the illiteracy rate, the per capita disposable income, the per capita public health budget expenditure, the number of medical insurance participants, and the balance of medical insurance fund revenue and expenditure. Local governments should scientifically formulate their own health planning by giving full attention to the radiation outcomes of the spatial spillover effect on the efficiency of medical and health resource allocation.

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Evaluation of Applied Artificial Neuronal Networks with a Timely Cervical Cancer Diagnosis in an Emerging Economy

Mota-López,

Barojas-Payán,

Hernández-Cisneros

et al. 2024

Management and Industrial Engineering

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How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

Cited by 8 publications

References 46 publications

Data driven approach for stochastic DEA in machine learning and artificial intelligence to improve the accuracy, stability, and interpretability of the model

Data driven approach for stochastic DEA in machine learning and artificial intelligence to improve the accuracy, stability, and interpretability of the model

The Efficiency of Inter-provincial Health Resource Allocation and its Influencing Factors in China---An Empirical Study Based on DEA Method and a Spatial Econometric Model

Evaluation of Applied Artificial Neuronal Networks with a Timely Cervical Cancer Diagnosis in an Emerging Economy

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