Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Áreas de investigación:

• Applications - Health

Año:

2020

Tipo de publicación:

Artículo

Palabras clave:

Hepatitis C Virus, Artificial Neural Networks, Typology of patients

Autores:

• Rivero-Juárez, Antonio
• Guijo-Rubio, David
• Téllez, Francisco
• Palacios, Rosario
• Merino, Dolores
• Macías, Juan
• Fernández, Juan Carlos
• Gutiérrez, Pedro Antonio
• Rivero, Antonio
• Hervás-Martínez, César

Journal:

PLoS One

Volumen:

15

Número:

1

Páginas:

e0227188

ISSN:

1932-6203

BibTex:

@article{VIH-ANN2020, author = "Antonio Rivero-Ju{\'a}rez and David Guijo-Rubio and Francisco T{\'e}llez and Rosario Palacios and Dolores Merino and Juan Mac{\'i}as and Juan Carlos Fern{\'a}ndez and Pedro Antonio Guti{\'e}rrez and Antonio Rivero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", awards = "JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", comments = "JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", doi = "10.1371/journal.pone.0227188", issn = "1932-6203", journal = "PLoS One", keywords = "Hepatitis C Virus, Artificial Neural Networks, Typology of patients", note = "JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES", number = "1", pages = "e0227188", title = "{U}sing machine learning methods to determine a typology of patients with {HIV}-{HCV} infection to be treated with antivirals", url = "doi.org/10.1371/journal.pone.0227188", volume = "15", year = "2020", }

Nota:

JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES

Comentarios:

JCR(2020): 3.240 Position: 26/73 (Q2) Category: MULTIDISCIPLINARY SCIENCES