  Optimization of patient throughput and wait time in emergency departments
(ED) is an important task for hospital systems. For that reason, Emergency
Severity Index (ESI) system for patient triage was introduced to help guide
manual estimation of acuity levels, which is used by nurses to rank the
patients and organize hospital resources. However, despite improvements that it
brought to managing medical resources, such triage system greatly depends on
nurse's subjective judgment and is thus prone to human errors. Here, we propose
a novel deep model based on the word attention mechanism designed for
predicting a number of resources an ED patient would need. Our approach
incorporates routinely available continuous and nominal (structured) data with
medical text (unstructured) data, including patient's chief complaint, past
medical history, medication list, and nurse assessment collected for 338,500 ED
visits over three years in a large urban hospital. Using both structured and
unstructured data, the proposed approach achieves the AUC of $\sim 88\%$ for
the task of identifying resource intensive patients (binary classification),
and the accuracy of $\sim 44\%$ for predicting exact category of number of
resources (multi-class classification task), giving an estimated lift over
nurses' performance by 16\% in accuracy. Furthermore, the attention mechanism
of the proposed model provides interpretability by assigning attention scores
for nurses' notes which is crucial for decision making and implementation of
such approaches in the real systems working on human health.
