Research & Publications

Here we feature the studies and collaborations that shape FedEMR.ai. These works explore how secure, privacy-preserving data networks and AI can improve healthcare, research, and clinical decision-making across institutions.

Our Publications

External Research

Our Publications

Papers authored or co-authored by the FedEMR.ai team

Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine

Authors: Li N, Lewin A, Ning S, Waito M, Zeller MP, Tinmouth A, et al. Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine. Transfusion. 2024. https://doi.org/10.1111/trf. 18077

Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study

Authors: Na Li, Kiarash Riazi, Jie Pan, Kednapa Thavorn, Jennifer Ziegler, Bram Rochwerg, Hude Quan, Hallie C. Prescott, Peter M. Dodek, Bing Li, Alain Gervais & Allan Garland

FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting

Authors: H Zhu, J Bai, N Li, X Li, D Liu, DL Buckeridge, Y Li
npj Digital Medicine 8 (1), 286

Federated blood supply chain demand forecasting: a case study

Authors: H Wei, N Li, J Wu, J Zhou, S Drew
International Workshop on Federated Learning for Distributed Data Mining

External Research

Peer-reviewed studies and supporting literature from external groups

External Research