Predicting 72-hour and 9-day return to the emergency department using machine learning.

ABSTRACT:

Objectives

To predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record.

Methods

This retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used

SUBMITTER: Hong WS 

PROVIDER: S-EPMC6951979 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature