A unified machine learning approach to time series forecasting applied
to demand at emergency departments
to demand at emergency departments
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
There were 25.6 million attendances at Emergency Departments (EDs) in England
in 2019 corresponding to an increase of 12 million attendances over the past
ten years. The steadily rising demand at EDs creates a constant challenge to
provide adequate quality of care while maintaining standards and productivity.
Managing hospital demand effectively requires an adequate knowledge of the
future rate of admission. Using 8 years of electronic admissions data from two
major acute care hospitals in London, we develop a novel ensemble methodology
that combines the outcomes of the best performing time series and machine
learning approaches in order to make highly accurate forecasts of demand, 1, 3
and 7 days in the future. Both hospitals face an average daily demand of 208
and 106 attendances respectively and experience considerable volatility around
this mean. However, our approach is able to predict attendances at these
emergency departments one day in advance up to a mean absolute error of +/- 14
and +/- 10 patients corresponding to a mean absolute percentage error of 6.8%
and 8.6% respectively. Our analysis compares machine learning algorithms to
more traditional linear models. We find that linear models often outperform
machine learning methods and that the quality of our predictions for any of the
forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In
addition to comparing and combining state-of-the-art forecasting methods to
predict hospital demand, we consider two different hyperparameter tuning
methods, enabling a faster deployment of our models without compromising
performance. We believe our framework can readily be used to forecast a wide
range of policy relevant indicators.
in 2019 corresponding to an increase of 12 million attendances over the past
ten years. The steadily rising demand at EDs creates a constant challenge to
provide adequate quality of care while maintaining standards and productivity.
Managing hospital demand effectively requires an adequate knowledge of the
future rate of admission. Using 8 years of electronic admissions data from two
major acute care hospitals in London, we develop a novel ensemble methodology
that combines the outcomes of the best performing time series and machine
learning approaches in order to make highly accurate forecasts of demand, 1, 3
and 7 days in the future. Both hospitals face an average daily demand of 208
and 106 attendances respectively and experience considerable volatility around
this mean. However, our approach is able to predict attendances at these
emergency departments one day in advance up to a mean absolute error of +/- 14
and +/- 10 patients corresponding to a mean absolute percentage error of 6.8%
and 8.6% respectively. Our analysis compares machine learning algorithms to
more traditional linear models. We find that linear models often outperform
machine learning methods and that the quality of our predictions for any of the
forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In
addition to comparing and combining state-of-the-art forecasting methods to
predict hospital demand, we consider two different hyperparameter tuning
methods, enabling a faster deployment of our models without compromising
performance. We believe our framework can readily be used to forecast a wide
range of policy relevant indicators.
Date Issued
2021-01-18
Date Acceptance
2020-12-16
Citation
BMC Emergency Medicine, 2021, 21 (9), pp.1-14
ISSN
1471-227X
Publisher
BioMed Central
Start Page
1
End Page
14
Journal / Book Title
BMC Emergency Medicine
Volume
21
Issue
9
Copyright Statement
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Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made
available in this article, unless otherwise stated in a credit line to the data.
credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative
Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made
available in this article, unless otherwise stated in a credit line to the data.
License URL
Sponsor
Imperial College Healthcare NHS Trust- BRC Funding
Medical Research Council (MRC)
Identifier
http://arxiv.org/abs/2007.06566v1
Grant Number
RDA02
MR/R015600/1
Subjects
stat.AP
stat.AP
cs.LG
stat.ML
Publication Status
Published
Date Publish Online
2021-01-18