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Villar J, González-Martín JM, Fernández C, Soler JA, Ambrós A, Pita-García L, Fernández L, Ferrando C, Arocas B, González-Vaquero M, et al. Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. Journal of Clinical Medicine. 2024; 13(6):1811. https://doi.org/10.3390/jcm13061811

Villar, Jesús, Jesús M. González-Martín, Cristina Fernández, Juan A. Soler, Alfonso Ambrós, Lidia Pita-García, Lorena Fernández, Carlos Ferrando, Blanca Arocas, Myriam González-Vaquero, and et al. 2024. "Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study" Journal of Clinical Medicine 13, no. 6: 1811. https://doi.org/10.3390/jcm13061811

Villar, J., González-Martín, J. M., Fernández, C., Soler, J. A., Ambrós, A., Pita-García, L., Fernández, L., Ferrando, C., Arocas, B., González-Vaquero, M., Añón, J. M., González-Higueras, E., Parrilla, D., Vidal, A., Fernández, M. M., Rodríguez-Suárez, P., Fernández, R. L., Gómez-Bentolila, E., Burns, K. E. A., ... the PredictION of Duration of mEchanical vEntilation in ARDS (PIONEER) Network. (2024). Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study. Journal of Clinical Medicine, 13(6), 1811. https://doi.org/10.3390/jcm13061811