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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
| 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 | Authors  : | Alvarez-Romero, Celia; ...; Kovacevic, Tomi S   ; Bokan, Darijo ; Hromis, Sanja ; Djekic-Malbasa, Jelena N ; Beslac, Suzana N; Zaric, Bojan ; ...; (broj, koautora 17) |
Issue Date: | 2022 | Publication: | JMIR MEDICAL INFORMATICS | ISSN: | 2291-9694 JMIR Medical Informatics Search Idenfier |
Type: | Article | Collation: | vol. 10 br. 6 str. Art. No e35307 | DOI: | 10.2196/35307 | WoS-ID: | 000809331100001 | Scopus-ID: | 2-s2.0-85133535592 | PMID: | 35653170 | PMCID: | PMC9204581 | URI: | https://enauka.gov.rs/handle/123456789/810615 | Project: | FAIR4Health project from the European Union's Horizon 2020 research and innovation program [824666] Carlos III National Institute of Health through the Programa de Ciencia de Datos de la Infraestructura de Medicina de Precision asociada a la Ciencia y la |
Metadata source: | (Preuzeto iz Nasi u WoS) | M-category: | 22M22 |
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