Citations refer to the original publication, not to a Scieee localized version.
Alvarez-Romero, C., Martinez-Garcia, A., Díaz-Jimènez, P., Jimènez-Juan, C., Nieto-Martín, M. D., Román Villarán, E., Ollero Baturone, M., & Parra Calderón, C. L. (2022). Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study. JMIR Publications. https://doi.org/10.2196/35307
Alvarez-Romero, Celia, et al. Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study. JMIR Publications, 2022. https://doi.org/10.2196/35307.
Alvarez-Romero, Celia, Alicia Martinez-Garcia, Pablo Díaz-Jimènez, Carlos Jimènez-Juan, María Dolores Nieto-Martín, Esther Román Villarán, Manuel Ollero Baturone, and Carlos Luis Parra Calderón. Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study. JMIR Publications, 2022. https://doi.org/10.2196/35307.
Alvarez-Romero, C. et al. (2022) Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study. JMIR Publications. Available at: https://doi.org/10.2196/35307.
C. Alvarez-Romero et al., “Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study,” JMIR Publications, 2022, doi: 10.2196/35307.
@misc{alvarezromero2022predicting,
author = {Alvarez-Romero, Celia and Martinez-Garcia, Alicia and Díaz-Jimènez, Pablo and Jimènez-Juan, Carlos and Nieto-Martín, María Dolores and Román Villarán, Esther and Ollero Baturone, Manuel and Parra Calderón, Carlos Luis},
title = {Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study},
year = {2022},
publisher = {JMIR Publications},
doi = {10.2196/35307},
url = {https://doi.org/10.2196/35307}
}
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