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

Abstract

Background 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. Objective 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). Methods 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. Results 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. Conclusions 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.

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

“…The clinical assessment concluded that from 100 recruited patients, the prediction was correct in 87% of cases (that is, in real-life, the patient was readmitted and the algorithm predicted that there was early 30-days hospital readmission risk; or the patient was not readmitted and the algorithm predicted that there was not early 30-days hospital readmission risk). The results and main findings of the second use case are been published (Open Access paper accepted and is currently in production) 13 .…”

The societal impact of Open Science: a scoping review

“…Throughout this study, we considered 250 texts focused on OFD (after title screening) that represented a diversity of research areas and aims but found that any claims of societal impact were speculative rather than based on observed and documented usage. For example, possible impacts included potential privacy violations [227,228], improvements in health research [229][230][231], or better monitoring of SDGs [232], yet evidence to back these claims was not presented.…”

FAIR principles to improve the impact on health research management outcomes

“…The FAIR principles aim to ensure that data is shared in a way that enables and enhances the reuse of information by humans and machines. Numerous researchers have analyzed the advantages of applying FAIR principles in the field of health [ 4 ] and especially in the research of patients with chronic diseases [ 5 , 6 ]. In fact, it is essential to refer to a 2018 European Commission report [ 7 ] on the costs of NOT having FAIR data.…”

“…Firstly, identification of multimorbidity patterns and polypharmacy correlation on the risk of mortality in elderly [ 5 ]. And second one, an early prediction service for 30-days readmission risk in patients with Chronic Obstructive Pulmonary Disease [ 6 ]. Concretely, the FAIR4Health platform included Privacy-Preserving Distributed Data Mining (PPDDM) methods to carry out a federated use of different AI algorithms, to identify association between de data (like the FP-Growth algorithm), and to perform predictions (like the Support Vector Machine, Logistic Regression, Decision Trees, Random Forest, Gradient Boosted Trees).…”

“…To validate and assess the FAIR4Health solution, after obtaining approvals from the local ethics committees of all clinical centres involved, a prospective observational study [ 6 ] was carried out between April 2021 and September 2021 related to the FAIR4Health pathfinder case studies.…”