Advancing In-Hospital Clinical Deterioration Prediction Models.

ABSTRACT:

Background

Early warning systems lack robust evidence that they improve patients' outcomes, possibly because of their limitation of predicting binary rather than time-to-event outcomes.

Objectives

To compare the prediction accuracy of 2 statistical modeling strategies (logistic regression and Cox proportional hazards regression) and 2 machine learning strategies (random forest and random survival forest) for in-hospital cardiopulmonary arrest.

Methods

Retrospective cohort study with prediction model development from deidentified electronic health records at an urban academic medical center.

Results

The classification models (logistic regression and random forest) had statistical recall and precision similar to or greater than those of the time-to-event models (

SUBMITTER: Jeffery AD 

PROVIDER: S-EPMC6141236 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature