Forecasting daily patient outflow from a ward having no real-time clinical data Gopakumar, Shivapratap, Tran, Truyen, Luo, Wei, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Forecasting daily patient outflow from a ward having no real-time clinical data, JMIR medical informatics, vol. 4, no. 3, pp. 1-16, doi: 10.2196/medinform.5650. Attached Files Name Description MIMEType Size Downloads gopakumar-forecastingdaily-2016.pdf Published version application/pdf 914.28KB 41 Title Forecasting daily patient outflow from a ward having no real-time clinical data Author(s) Gopakumar, Shivapratap Tran, Truyen orcid.org/0000-0001-6531-8907 Luo, Wei orcid.org/0000-0002-4711-7543 Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Journal name JMIR medical informatics Volume number 4 Issue number 3 Article ID e25 Start page 1 End page 16 Total pages 16 Publisher JMIR Publications Place of publication Toronto, Ont. Publication date 2016 ISSN 2291-9694 Keyword(s) discharge planning patient flow predictive models Summary OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. RESULTS: Our data consisted of 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 with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments. Language eng DOI 10.2196/medinform.5650 Field of Research 080109 Pattern Recognition and Data Mining 111716 Preventive Medicine Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2016, The Authors Free to Read? Yes Use Rights Persistent URL http://hdl.handle.net/10536/DRO/DU:30085642 Document type: Journal Article Collections: School of Information Technology Centre for Pattern Recognition and Data Analytics Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Wed, 24 Aug 2016, 11:44:51 EST Thu, 25 Aug 2016, 17:02:24 EST Fri, 26 Aug 2016, 05:00:14 EST Fri, 26 Aug 2016, 06:09:20 EST Mon, 17 Oct 2016, 07:35:02 EST Mon, 17 Oct 2016, 07:38:53 EST Mon, 17 Oct 2016, 07:41:10 EST Fri, 31 Mar 2017, 11:09:52 EST Fri, 07 Sep 2018, 12:30:56 EST Sat, 05 Jan 2019, 01:30:18 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 268 Abstract Views, 42 File Downloads  -  Detailed Statistics Created: Thu, 25 Aug 2016, 17:02:24 EST

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.