A better project performance prediction model using fuzzy time series and data envelopment analysis

2016 ◽  
Vol 67 (10) ◽  
pp. 1274-1287 ◽  
Author(s):  
Mostafa Salari ◽  
Homayoun Khamooshi
Keyword(s):  
Time Series ◽  
Data Envelopment Analysis ◽  
Prediction Model ◽  
Performance Prediction ◽  
Project Performance ◽  
Fuzzy Time Series ◽  
Data Envelopment

Project performance prediction model linking agility and flexibility demands to project type

2021 ◽  
Author(s):  
Marco Aurélio Oliveira ◽  
Luiz V. O. Dalla Valentina ◽  
André Hideto Futami ◽  
Osmar Possamai ◽  
Carlos Alberto Flesch
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Project Performance

scholarly journals A hybrid data envelopment analysis—artificial neural network prediction model for COVID-19 severity in transplant recipients

2021 ◽  
Author(s):  
Ignacio Revuelta ◽  
Francisco J. Santos-Arteaga ◽  
Enrique Montagud-Marrahi ◽  
Pedro Ventura-Aguiar ◽  
Debora Di Caprio ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Envelopment Analysis ◽  
Hospital Admission ◽  
Prediction Model ◽  
Data Envelopment ◽  
Transplant Recipients ◽  
Ann Model ◽  
Hybrid Data ◽  
Artificial Neural

AbstractIn an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)—Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model.

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