Federated learning of medical concepts embedding using BEHRT.

ABSTRACT:

Objectives

Electronic health record data is often considered sensitive medical information. Therefore, the EHR data from different medical centers often cannot be shared, making it difficult to create prediction models using multicenter EHR data, which is essential for such models' robustness and generalizability. Federated learning (FL) is an algorithmic approach that allows learning a shared model using data in multiple locations without the need to store all data in a single central place. Our study aims to evaluate an FL approach using the BEHRT model for predictive tasks on EHR data, focusing on next visit prediction.

Materials and methods

We propose an FL approach for learning medical concepts embedding. This pretrained model can be used for fine-tuning for specific do

SUBMITTER: Ben Shoham O 

PROVIDER: S-EPMC11498200 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature