Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24207, first published .
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach

Journals

  1. Adamidi E, Mitsis K, Nikita K. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Computational and Structural Biotechnology Journal 2021;19:2833 View
  2. Kaissis G, Ziller A, Passerat-Palmbach J, Ryffel T, Usynin D, Trask A, Lima I, Mancuso J, Jungmann F, Steinborn M, Saleh A, Makowski M, Rueckert D, Braren R. End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence 2021;3(6):473 View
  3. Born J, Beymer D, Rajan D, Coy A, Mukherjee V, Manica M, Prasanna P, Ballah D, Guindy M, Shaham D, Shah P, Karteris E, Robertus J, Gabrani M, Rosen-Zvi M. On the role of artificial intelligence in medical imaging of COVID-19. Patterns 2021;2(6):100269 View
  4. Zheng Z, Zhou Y, Sun Y, Wang Z, Liu B, Li K. Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connection Science 2021:1 View