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

BackgroundOwing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, I...

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Main Authors: Celia Alvarez-Romero, Alicia Martinez-Garcia, Jara Ternero Vega, Pablo Díaz-Jimènez, Carlos Jimènez-Juan, María Dolores Nieto-Martín, Esther Román Villarán, Tomi Kovacevic, Darijo Bokan, Sanja Hromis, Jelena Djekic Malbasa, Suzana Beslać, Bojan Zaric, Mert Gencturk, A Anil Sinaci, Manuel Ollero Baturone, Carlos Luis Parra Calderón
Format: Article
Language:English
Published: JMIR Publications 2022-06-01
Series:JMIR Medical Informatics
Online Access:https://medinform.jmir.org/2022/6/e35307

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https://medinform.jmir.org/2022/6/e35307

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