Assessing the transportability of clinical prediction models for cognitive impairment using causal models.

ABSTRACT:

Background

Machine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a new setting without available data is challenging. We aimed to investigate the transportability by calibration and discrimination of prediction models for cognitive impairment in simulated external settings with different distributions of demographic and clinical characteristics.

Methods

We mapped and quantified relationships between variables associated with cognitive impairment using causal graphs, structural equation models, and data from the ADNI study. These estimates were then used to generate datasets and evaluate prediction models with different sets of predictors. We measured transportability to external settings unde

SUBMITTER: Fehr J 

PROVIDER: S-EPMC10439645 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature