Transfer-learning on federated observational healthcare data for prediction models using Bayesian sparse logistic regression with informed priors.

ABSTRACT:

Objective

To develop a transfer-learning Bayesian sparse logistic regression model that transfers information learned from one dataset to another by using an informed prior to facilitate model fitting in small-sample clinical patient-level prediction problems that suffer from a lack of available information.

Methods

We propose a Bayesian framework for prediction using logistic regression that aims to conduct transfer-learning on regression coefficient information from a larger dataset model (order 105-106 patients by 105 features) into a small-sample model (order 103 patients). Our approach imposes an informed, hierarchical prior on each regression coefficient defined as a discrete mixture of the Bayesian Bridge shrinkage prior and an informed normal distribution. Performanc

SUBMITTER: Li KM 

PROVIDER: S-EPMC12844582 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature