RT Journal Article
T1 Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals.
A1 Rivero-Juarez, Antonio
A1 Guijo-Rubio, David
A1 Tellez, Francisco
A1 Palacios, Rosario
A1 Merino, Dolores
A1 Macias, Juan
A1 Fernandez, Juan Carlos
A1 Gutierrez, Pedro Antonio
A1 Rivero, Antonio
A1 Hervas-Martinez, Cesar
AB Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group.
PB Public Library of Science
YR 2020
FD 2020-01-10
LK http://hdl.handle.net/10668/14940
UL http://hdl.handle.net/10668/14940
LA en
NO Rivero-Juárez A, Guijo-Rubio D, Tellez F, Palacios R, Merino D, Macías J, et al. Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals. PLoS One. 2020 Jan 10;15(1):e0227188
DS RISalud
RD Apr 8, 2025