Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling patient flow in the emergency department using machine learning
  and simulation   Recently, the combination of machine learning (ML) and simulation is gaining
a lot of attention. This paper presents a novel application of ML within the
simulation to improve patient flow within an emergency department (ED). An ML
model used within a real ED simulation model to quantify the effect of
detouring a patient out of the ED on the length of stay (LOS) and
door-to-doctor time (DTDT) as a response to the prediction of patient admission
to the hospital from the ED. The ML model trained using a set of six features
including the patient age, arrival day, arrival hour of the day, and the triage
level. The prediction model used a decision tree (DT) model, which is trained
using historical data achieves a 75% accuracy. The set of rules extracted from
the DT are coded within the simulation model. Given a certain probability of
free inpatient beds, the predicted admitted patient is then pulled out from the
ED to inpatient units to alleviate the crowding within the ED. The used policy
combined with adding specific ED resources achieve 9.39% and 8.18% reduction in
LOS and DTDT, respectively.

5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga