A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis.

ABSTRACT: An increasingly important data source for the development of clinical risk prediction models is electronic health records (EHRs). One of their key advantages is that they contain data on many individuals collected over time. This allows one to incorporate more clinical information into a risk model. However, traditional methods for developing risk models are not well suited to these irregularly collected clinical covariates. In this paper, we compare a range of approaches for using longitudinal predictors in a clinical risk model. Using data from an EHR for patients undergoing hemodialysis, we incorporate five different clinical predictors into a risk model for patient mortality. We consider different approaches for treating the repeated measurements including use of summary statistics, ma

SUBMITTER: Goldstein BA 

PROVIDER: S-EPMC5494276 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature