Predicting hospital admission at emergency department triage using machine learning.

ABSTRACT:

Objective

To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.

Methods

This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential b

SUBMITTER: Hong WS 

PROVIDER: S-EPMC6054406 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature