Alvarez-Romero Celia, Martinez-Garcia Alicia, Ternero Vega Jara, Díaz-Jimènez Pablo, Jimènez-Juan Carlos, Nieto-Martín María Dolores, Román Villarán Esther, Kovacevic Tomi, Bokan Darijo, Hromis Sanja, Djekic Malbasa Jelena, Beslać Suzana, Zaric Bojan, Gencturk Mert, Sinaci A Anil, Ollero Baturone Manuel, Parra Calderón Carlos Luis
Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain.
Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain.
JMIR Med Inform. 2022 Jun 2;10(6):e35307. doi: 10.2196/35307.
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.
由于健康数据的特性，其在研究中的共享和再利用受到法律、技术和伦理问题的限制。从这个意义上讲，为应对这一挑战并促进科学知识的发现，可查找、可访问、可互操作和可重用（FAIR）原则有助于组织以安全、适当且有用的方式为其他研究人员共享研究数据。
本研究的目的是使现有的健康研究数据集符合FAIR原则，并在不同健康研究执行组织的FAIR化数据集之上应用联邦机器学习架构。通过评估用于慢性阻塞性肺疾病（COPD）患者30天再入院风险实时预测的联邦模型，对整个FAIR4Health解决方案进行验证。
在3种不同医疗环境中对健康研究数据集应用FAIR原则，促成了一项回顾性多中心研究，以开发用于早期预测COPD患者30天再入院风险的特定联邦机器学习模型。该预测模型是在FAIR4Health平台上生成的。最后，在来自不同国家的2个医疗中心进行了一项为期30天随访的观察性前瞻性研究。回顾性和前瞻性研究均使用相同的纳入和排除标准。
通过在不同健康研究执行组织的FAIR化数据集之上实施联邦机器学习模型，证明了临床有效性。使用来自4944例COPD患者的回顾性数据训练了预测30天医院再入院风险的联邦模型。在2021年4月至2021年9月进行的观察性前瞻性研究中，使用100例招募患者（22例来自西班牙，78例来自塞尔维亚）的数据对预测模型进行评估，这100例患者是从2070例观察（查看记录）患者中选取的。观察到在FAIR4Health平台上生成的预测模型具有显著的准确性（0.98）和精确性（0.25）。因此，在87%（87/100）的病例中确认了30天再入院风险的预测结果。
在健康研究执行组织中实施FAIR数据政策以促进数据共享和再利用，在健康研究数据的发现、访问、整合和分析之后是相关且必要的。FAIR4Health项目在健康领域提出了一种技术解决方案，以促进与FAIR原则保持一致。
