Predicting mortality over different time horizons: which data elements are needed?

ABSTRACT:

Objective

Electronic health records (EHRs) are a resource for "big data" analytics, containing a variety of data elements. We investigate how different categories of information contribute to prediction of mortality over different time horizons among patients undergoing hemodialysis treatment.

Material and methods

We derived prediction models for mortality over 7 time horizons using EHR data on older patients from a national chain of dialysis clinics linked with administrative data using LASSO (least absolute shrinkage and selection operator) regression. We assessed how different categories of information relate to risk assessment and compared discrete models to time-to-event models.

Results

The best predictors used all the available data (c-statistic ranged from 0.72

SUBMITTER: Goldstein BA 

PROVIDER: S-EPMC5201182 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature