  Accurately predicting hospital length-of-stay at the time a patient is
admitted to hospital may help guide clinical decision making and resource
allocation. In this study we aim to build a decision support system that
predicts hospital length-of-stay for patients admitted to general internal
medicine from the emergency department. We conduct an exploratory data analysis
and employ feature selection methods to identify the attributes that result in
the best predictive performance. We also develop a discrete-event simulation
model to assess the performances of the prediction models in a practical
setting. Our results show that the recommendation performances of the proposed
approaches are generally acceptable and do not benefit from the feature
selection. Further, the results indicate that hospital length-of-stay could be
predicted with reasonable accuracy (e.g., AUC value for classifying short and
long stay patients is 0.69) using patient admission demographics, laboratory
test results, diagnostic imaging, vital signs and clinical documentation.
