Predicting Elevated Risk of Hospitalization Following Emergency Department Discharges

TL;DR

This paper demonstrates how data mining models can accurately predict hospitalizations shortly after emergency department discharges, aiding in early intervention and improving patient safety.

Contribution

It introduces an ensemble of interpretable classifiers that predict early hospitalizations post-ED discharge with high accuracy, facilitating practical clinical decision-making.

Findings

High prediction accuracy within 3, 7, and 14 days

Models are interpretable and operationally useful

Ensemble approach improves prediction performance

Abstract

Hospitalizations that follow closely on the heels of one or more emergency department visits are often symptoms of missed opportunities to form a proper diagnosis. These diagnostic errors imply a failure to recognize the need for hospitalization and deliver appropriate care, and thus also bear important connotations for patient safety. In this paper, we show how data mining techniques can be applied to a large existing hospitalization data set to learn useful models that predict these upcoming hospitalizations with high accuracy. Specifically, we use an ensemble of logistics regression, na\"ive Bayes and association rule classifiers to successfully predict hospitalization within 3, 7 and 14 days of an emergency department discharge. Aside from high accuracy, one of the advantages of the techniques proposed here is that the resulting classifier is easily inspected and interpreted by humans so that the learned rules can be readily operationalized. These rules can then be easily distributed and applied directly by physicians in emergency department settings to predict the risk of early admission prior to discharging their emergency department patients.