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Methods Inf Med. 2017 Oct 26;56(5):377-389. doi: 10.3414/ME17-01-0024. Epub 2017 Aug 16.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

Author information

1
Justin D. Schrager, MD, MPH, Emory University School of Medicine, Department of Emergency Medicine, 531 Asbury Circle, Annex Building N340, Atlanta, GA 30322, USA, E-mail: jschrag@emory.edu.

Abstract

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 0.731- 0.753) 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; emergency department; logistic regression; neural networks; prediction

PMID:
28816338
DOI:
10.3414/ME17-01-0024
[Indexed for MEDLINE]

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