RT Journal Article
T1 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.
A1 Alvarez-Romero, Celia
A1 Martinez-Garcia, Alicia
A1 Ternero Vega, Jara
A1 Díaz-Jimènez, Pablo
A1 Jimènez-Juan, Carlos
A1 Nieto-Martín, María Dolores
A1 Román Villarán, Esther
A1 Kovacevic, Tomi
A1 Bokan, Darijo
A1 Hromis, Sanja
A1 Djekic Malbasa, Jelena
A1 Beslać, Suzana
A1 Zaric, Bojan
A1 Gencturk, Mert
A1 Sinaci, A Anil
A1 Ollero Baturone, Manuel
A1 Parra Calderón, Carlos Luis
K1 FAIR principles
K1 chronic obstructive pulmonary disease
K1 clinical validation
K1 early predictive model
K1 privacy-preserving distributed data mining
K1 research data management
AB Owing 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, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.
SN 2291-9694
YR 2022
FD 2022-06-02
LK http://hdl.handle.net/10668/20480
UL http://hdl.handle.net/10668/20480
LA en
DS RISalud
RD Apr 7, 2025