Data heterogeneity in federated learning with electronic health records: case studies of risk prediction for acute kidney injury and sepsis diseases in critical care

HIGHLIGHTS

• who: Suraj Rajendran and colleagues from the Editor: GFrasch, University of Washington have published the Article: Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care, in the Journal: (JOURNAL) of March/15,/2023
• what: The authors take AKI and sepsis onset risk prediction in ICU as two examples to explore the impact of heterogeneity in the FL framework as well as compare performances across frameworks. Learning algorithm To investigate the effects of heterogeneity across architectures, the authors focused on two . . .
  

   

  

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