ABSTRACT:
Crowding within emergency departments
(EDs) can have significant negative consequences for patients. EDs therefore
need to explore the use of innovative methods to improve patient flow and
prevent overcrowding. One potential method is the use of data mining using
machine learning techniques to predict ED admissions. This paper uses routinely
collected administrative data (120 600 records) from two major acute hospitals
in Northern Ireland to compare contrasting machine learning algorithms in
predicting the risk of admission from the ED. We use three algorithms to build
the predictive models: 1) logistic regression; 2) decision trees; and 3)
gradient boosted machines (GBM). The GBM performed better (accuracy D 80:31%,
AUC-ROC D 0:859) than the decision tree (accuracy D 80:06%, AUC-ROC D 0:824)
and the logistic regression model (accuracy D 79:94%, AUC-ROC D 0:849). Drawing
on logistic regression, we identify several factors related to hospital
admissions, including hospital site, age, arrival mode, triage category, care
group, previous admission in the past month, and previous admission in the past
year. This paper highlights the potential utility of three common machine learning
algorithms in predicting patient admissions. Practical implementation of the
models developed in this paper in decision support tools would provide a
snapshot of predicted admissions from the ED at a given time, allowing for
advance resource planning and the avoidance bottlenecks in patient flow, as
well as comparison of predicted and actual admission rates. When
interpretability is a key consideration, EDs should consider adopting logistic
regression models, although GBM’s will be useful where accuracy is paramount.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
BYRON GRAHAM,
RAYMOND BOND, MICHAEL QUINN, AND MAURICE MULVENNA, “Using Data Mining to
Predict Hospital Admissions From the Emergency
Department”, IEEE Access, 2018.