Balancing Uneven Knowledge of Hospital Nodes for ICU Patients Diagnosis through Federated Learning

The Covid pandemic highlighted the urgent need for collaborations in the healthcare sector to empower clinical and scientific communities in responding to global challenges. In this context, the ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a European Telemedicine Network, allowing for sharing of capabilities, knowledge, and expertise distributed in such a network. Nevertheless, healthcare data sharing has ethical, regulatory, and legal complexities imposing restrictions on access and use. In addition, data and knowledge are very often unevenly distributed at the different nodes of the network depending on their geographical location and dimension. To address these issues, a federated learning architecture is proposed to allow for distributed machine learning within the cross-institutional healthcare system without moving data outside its original location. The approach has been applied for the early prediction of high-risk hypertension patients. The experimentation carried out shows that the knowledge of single nodes is spread within the federation, improving the ability of each of them to perform predictions also on not previously treated cases. The performance evaluation of the computed predictions in terms of accuracy and precision is over 0.91 confirming the encouraging results of the proposed FL approach.