Published on 2.6.2022 in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35307, first published November 30, 2021.

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

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

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

Celia Alvarez-Romero¹

; Alicia Martinez-Garcia¹

; Jara Ternero Vega²

; Pablo Díaz-Jimènez²

; Carlos Jimènez-Juan²

; María Dolores Nieto-Martín²

; Esther Román Villarán¹

; Tomi Kovacevic^{3, 4}

; Darijo Bokan³

; Sanja Hromis^{3, 4}

; Jelena Djekic Malbasa^{3, 4}

; Suzana Beslać³

; Bojan Zaric^{3, 4}

; Mert Gencturk⁵

; A Anil Sinaci⁵

; Manuel Ollero Baturone²

; Carlos Luis Parra Calderón¹

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