  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.
