Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability.

ABSTRACT:

Background

Prognostic models that are accurate could help aid medical decision making. Large observational databases often contain temporal medical data for large and diverse populations of patients. It may be possible to learn prognostic models using the large observational data. Often the performance of a prognostic model undesirably worsens when transported to a different database (or into a clinical setting). In this study we investigate different ensemble approaches that combine prognostic models independently developed using different databases (a simple federated learning approach) to determine whether ensembles that combine models developed across databases can improve model transportability (perform better in new data than single database models)?

Methods

For a give

SUBMITTER: Reps JM 

PROVIDER: S-EPMC9134686 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature