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

Shivapratap Gopakumar, Truyen Tran, Wei Luo, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI
Subtitle of host publication4–7 October 2016 Chicago, Illinois
EditorsKai Zheng, Gregor Stiglic, Ann Blandford
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781509061174
ISBN (Print)9781509061181
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Conference on Healthcare Informatics 2016 - Chicago, United States of America
Duration: 4 Oct 20167 Oct 2016 (Proceedings)


ConferenceIEEE International Conference on Healthcare Informatics 2016
Abbreviated titleICHI 2016
Country/TerritoryUnited States of America
Internet address

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