A hybrid data envelopment analysis-artificial neural network prediction model for COVID-19 severity in transplant recipients.
Artif Intell Rev
; 54(6): 4653-4684, 2021.
Article
in English
| MEDLINE | ID: covidwho-1202775
ABSTRACT
In 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. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10008-0.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
Artif Intell Rev
Year:
2021
Document Type:
Article
Affiliation country:
S10462-021-10008-0
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