{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li KM"],"funding":["US National Institutes of Health","Put VA Data to Work for Veterans","US Department of Veterans Affairs under the research priority to “Put VA Data to Work for Veterans”","NHLBI NIH HHS","US Department of Veterans Affairs under the research priority to \"Put VA Data to Work for Veterans\"","NIH HHS","US Department of Veterans Affairs"],"pagination":["409-423"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12844582"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["33(2)"],"pubmed_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"],"journal":["Journal of the American Medical Informatics Association : JAMIA"],"pubmed_title":["Transfer-learning on federated observational healthcare data for prediction models using Bayesian sparse logistic regression with informed priors."],"pmcid":["PMC12844582"],"funding_grant_id":["R01 HL169954","22-D4V","VA ORD 22-D4V"],"pubmed_authors":["Schuemie MJ","Suchard MA","Li KM","Reps JM","Nishimura A"],"additional_accession":[]},"is_claimable":false,"name":"Transfer-learning on federated observational healthcare data for prediction models using Bayesian sparse logistic regression with informed priors.","description":"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","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-06-09T03:18:35.616Z","creation":"2026-06-09T03:11:50.374Z"},"accession":"S-EPMC12844582","cross_references":{"pubmed":["41144298"],"doi":["10.1093/jamia/ocaf146"]}}