TY  - JOUR 
AU  - Marcos Martín, Miguel
AU  - Belhassen-García, Moncef
AU  - Sánchez-Puente, Antonio
AU  - Sampedro-Gómez, Jesús
AU  - Azibeiro, Raúl
AU  - Dorado Díaz, Pedro Ignacio
AU  - Marcano-Millán, Edgar
AU  - García-Vidal, Carolina
AU  - Moreiro Barroso, María Teresa
AU  - Cubino Bóveda, Noelia
AU  - Pérez-García, María-Luisa
AU  - Rodríguez-Alonso, Beatriz
AU  - Encinas-Sánchez, Daniel
AU  - Peña-Balbuena, Sonia
AU  - Sobejano-Fuertes, Eduardo
AU  - Inés, Sandra
AU  - Carbonell, Cristina
AU  - López Parra, Miriam
AU  - Andrade-Meira, Fernanda
AU  - López-Bernús, Amparo
AU  - Lorenzo, Catalina
AU  - Carpio, Adela
AU  - Polo-San-Ricardo, David
AU  - Sánchez Hernández, Miguel Vicente
AU  - Borrás Beato, Rafael
AU  - Sagredo-Meneses, Víctor
AU  - Sánchez Fernández, Pedro Luis
AU  - Soriano, Alex
AU  - Martín Oterino, José Ángel
PY  - 2021
UR  - http://hdl.handle.net/10366/161982
AB  - [EN]Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.
We trained, validated, and externally tested a machine-learning model to early identify...
LA  - eng
PB  - Public Library of Science (PLOS)
KW  - Machine learning
KW  - Covid-19
KW  - Disease score
KW  - Hospitalized
KW  - Artificial intelligence
KW  - SARS-CoV-2
KW  - Area Under Curve
KW  - Aged
KW  - Adult
KW  - Risk Assessment
KW  - Forecasting
KW  - Humans
KW  - Middle Aged
KW  - Hospitalization
KW  - Severity of Illness Index
KW  - Respiration
KW  - Cohort Studies
KW  - ROC Curve
KW  - Retrospective Studies
TI  - Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.
DO  - 10.1371/journal.pone.0240200
T2  - PloS one
VL  - 16
M2  - e0240200
ER  -