Summary
Objective: To describe and compare logistic regression and neural network modeling strategies
to predict hospital admission or transfer following initial presentation to Emergency
Department (ED) triage with and without the addition of natural language processing
elements.
Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a
cross-sectional probability sample of United States EDs from 2012 and 2013 survey
years, we developed several predictive models with the outcome being admission to
the hospital or transfer vs. discharge home. We included patient characteristics immediately
available after the patient has presented to the ED and undergone a triage process.
We used this information to construct logistic regression (LR) and multilayer neural
network models (MLNN) which included natural language processing (NLP) and principal
component analysis from the patient’s reason for visit. Ten-fold cross validation
was used to test the predictive capacity of each model and receiver operating curves
(AUC) were then calculated for each model.
Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission
(or transfer). A total of 48 principal components were extracted by NLP from the reason
for visit fields, which explained 75% of the overall variance for hospitalization.
In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830)
for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including
only free-text information generated AUC of 0.742 (95% CI 0.7310.753) for logistic
regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables
and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853)
for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN.
Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented
to ED triage overall was good, and was improved with the inclusion of free text data
from a patient’s reason for visit regardless of modeling approach. Natural language
processing and neural networks that incorporate patient-reported outcome free text
may increase predictive accuracy for hospital admission.
Keywords
Natural language processing - neural networks - logistic regression - prediction -
emergency department
