Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies.

ABSTRACT:

Background

Federated learning (FL) is a technique for learning prediction models without sharing records between hospitals. Compared to centralized training approaches, the adoption of FL could negatively impact model performance.

Aim

This study aimed to evaluate four types of multicenter model development strategies for predicting 30-day mortality for patients undergoing transcatheter aortic valve implantation (TAVI): (1) central, learning one model from a centralized dataset of all hospitals; (2) local, learning one model per hospital; (3) federated averaging (FedAvg), averaging of local model coefficients; and (4) ensemble, aggregating local model predictions.

Methods

Data from all 16 Dutch TAVI hospitals from 2013 to 2021 in the

SUBMITTER: Yordanov TR 

PROVIDER: S-EPMC11257923 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature