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Forecasting patient outflow from wards having no real-time clinical data
conference contribution
posted on 2016-12-06, 00:00 authored by Shivapratap Gopakumar, Truyen TranTruyen Tran, Wei LuoWei Luo, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshModelling 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.
History
Event
Healthcare Informatics. IEEE International Conference (2016 : Chicago, Illinois)Pagination
177 - 183Publisher
IEEELocation
Chicago, IllinoisPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2016-10-04End date
2016-10-07ISBN-13
9781509061174Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, IEEEEditor/Contributor(s)
W Fu, L Hodges, K Zheng, G Stiglic, A BlandfordTitle of proceedings
ICHI 2016: Proceedings of the IEEE International Conference on Healthcare InformaticsUsage metrics
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