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

Misiunas, Nicholas; Oztekin, Asil; Chen, Yao; Chandra, Kavitha

doi:10.1016/j.omega.2015.03.010

Cited by 71 publications

(54 citation statements)

References 29 publications

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“…The second axis is related to reformulation of DEA models to a dynamic context as well as the hybridization of the proposed DDEA models with mathematical techniques. In this case, we can verify some studies with: Fuzzy and multi-purpose programming (Jafarian-Moghaddam and Ghoseiri, 2012); Multi-period Range-Adjusted Measure (Avkiran and Goto, 2011); DEA Malmquist (Emrouznejad and Thanassoulis, 2010); Dynamic Directional Distance Function (Kapelko et al, 2014); DEA Slacks-Based Measure model (Tone and Tsutsui, 2010); Network DEA , and also suggest new research's frontiers with DEA with Artificial Neural Networks (Misiunas et al, 2016); Stochastic Frontier Analysis (Lampe and Hilgers, 2015), among others.…”

Section: Future Research Directionssupporting

confidence: 54%

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Mariz

Almeida

Aloise

2017

Int Trans Operational Res

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This article reports the evolution of the literature on Dynamic Data Envelopment Analysis (DDEA) models from 1996 to 2016. Systematic searches in the databases Scopus and Web of Science were performed to outline the state of the art. The results enabled the establishment of DDEA studies as the scope of this article, analyzing the transition elements to represent temporal interdependence. The categorization of these studies enabled the mapping of the evolution of the DDEA literature and identification of the relationships between models. The three most widely adopted studies to conduct DDEA research were classified as structuring models. Mapping elucidated the literature behavior through three phases and showed an increase in publications with applications in recent years. The analysis of applications indicated that most studies address evaluations in the agriculture and farming, banking and energy sectors and consider the facilities as transition elements between analysis periods.

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Section: Future Research Directionssupporting

confidence: 54%

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Mariz

Almeida

Aloise

2017

Int Trans Operational Res

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“…Specifically, neural networks are trained to assess how these variables could be used as predictors of efficiency in ASEAN banks. There is an emerging literature on the application of ANNs on efficiency scores derived from non‐parametric methods, such as DEA (Misiunas et al ., ) and TOPSIS (Barros & Wanke, ), for predictive or classificatory purposes.…”

Section: Methodsmentioning

confidence: 99%

“…There is an emerging literature on the application of ANNs on efficiency scores derived from non-parametric methods, such as DEA (Misiunas et al, 2015) and TOPSIS (Barros & Wanke, 2015), for predictive or classificatory purposes. Neural networks are defined by important parameters that cannot be estimated from the data in a direct fashion (Palomares-Salas et al, 2014).…”

Section: Neural Networkmentioning

confidence: 99%

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

Wänke

Azad

Barros³

et al. 2015

Expert Systems

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This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.

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“…It is widely used in various sectors recently (Alper et al 2015;LaPlante and Paradi 2015;Misiunas et al 2015;Zografidou et al 2015) as well as academia. DEA gives an efficiency score by dealing with multiple inputs and multiple outputs.…”

Section: Dea Methodologymentioning

confidence: 99%

An Efficiency Evaluation Model for Academic Faculties of a Leading University by Data Envelopment Analysis

Gül

Yücesan

Duman

2017

isarder

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Data envelopment analysis (DEA) is a linear programming based data analytic method for measuring the relative efficiency of organisational units where the presence of multiple inputs and outputs makes comparisons difficult. Academic departments have critical importance for a university so we agree to research and compare about academic faculties in a leading university in Turkey. The aim of the study is to measure the relative efficiency of the academic faculties and determine the efficient/inefficient ones in the studied university. 12 faculties of the university are investigated within the scope of this study. The input variables are considered as total number of academic staff, total number of non-academic staff, number of students and the output variables are as number of publications, number of projects and the percentage of budget used. While results of CCR model show an average of 90.5% relative efficiency value, five faculties are found 100% efficient according to the CCR model. According to BCC model, the results have an average of 93.7% and 6 faculties are 100% efficient. In terms of the potential improvements evaluated for each inefficient faculty, it is seen that faculty of mechanical engineering and faculty of civil engineering are the ones requiring the most improvement. This paper contributes to the literature a lot however it is a new and proper study on efficiency analysis of faculties of a Turkish university. On conclusion of the DEA efficiency scores, the existence of misallocation of resources or/and inefficient applications to the faculties' academic development are uncovered.

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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

Cited by 71 publications

References 29 publications

A review of Dynamic Data Envelopment Analysis: state of the art and applications

A review of Dynamic Data Envelopment Analysis: state of the art and applications

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

An Efficiency Evaluation Model for Academic Faculties of a Leading University by Data Envelopment Analysis

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