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Forecasting patient outflow from wards having no real-time clinical data

Gopakumar, Shivapratap, Tran, Truyen, Luo, Wei, Phung, Dinh and Venkatesh, Svetha 2016, Forecasting patient outflow from wards having no real-time clinical data, in ICHI 2016: Proceedings of the IEEE International Conference on Healthcare Informatics, IEEE, Piscataway, N.J., pp. 177-183, doi: 10.1109/ICHI.2016.26.

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Title Forecasting patient outflow from wards having no real-time clinical data
Author(s) Gopakumar, Shivapratap
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Healthcare Informatics. IEEE International Conference (2016 : Chicago, Illinois)
Conference location Chicago, Illinois
Conference dates 4-7 Oct. 2016
Title of proceedings ICHI 2016: Proceedings of the IEEE International Conference on Healthcare Informatics
Editor(s) Fu, W.T.
Hodges, L.
Zheng, K.
Stiglic, G.
Blandford, A.
Publication date 2016
Conference series Healthcare Informatics IEEE International Conference
Start page 177
End page 183
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
Summary Modelling patient flow is crucial in understanding resource demand and prioritization. To date, there has been limited work in predicting ward-level discharges. Our study investigates forecasting total next-day discharges from an open ward. In the absence of real-time clinical data, we propose to construct a feature set from patient demographics, ward data and discharge time series to derive a random forest model for forecasting daily discharge. Using data from a general ward of a large regional Australian hospital, we compared our random forest model with a classical auto-regressive integrated moving average (ARIMA) for 12,141 patient visits over 1826 days. Forecasting quality was measured using Mean Forecast Error, Mean Absolute Error, symmetric Mean Absolute Percentage Error and Root Mean Square Error. When compared to the baseline model, next day discharge forecasts using random forests achieved 17.4 % improvement in Mean Absolute Error, for all days in the year 2014.
ISBN 9781509061174
Language eng
DOI 10.1109/ICHI.2016.26
Field of Research 080109 Pattern Recognition and Data Mining
080702 Health Informatics
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30091397

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.