{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:20:07Z","timestamp":1767831607431,"version":"3.49.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>The emergency department of a hospital plays an extremely important role in the healthcare of patients. To maintain a high quality service, clinical professionals need information on how patient flow will evolve in the immediate future. With accurate emergency department forecasts it is possible to better manage available human resources by allocating clinical staff before peak periods, thus preventing service congestion, or releasing clinical staff at less busy times. This paper describes a solution developed for the presentation of hourly, four-hour, eight-hour and daily number of admissions to a hospital\u2019s emergency department. A 10-year history (2009\u20132018) of the number of emergency admissions in a Portuguese hospital was used. To create the models several methods were tested, including exponential smoothing, SARIMA, autoregressive and recurrent neural network, XGBoost and ensemble learning. The models that generated the most accurate hourly time predictions were the recurrent neural network with one-layer (sMAPE = 23.26%) and with three layers (sMAPE = 23.12%) and XGBoost (sMAPE = 23.70%). In terms of efficiency, the XGBoost method has by far outperformed all others. The success of the recurrent neuronal network and XGBoost machine learning methods applied to the prediction of the number of emergency department admissions has been demonstrated here, with an accuracy that surpasses the models found in the literature.<\/jats:p>","DOI":"10.3233\/ida-205390","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T20:04:07Z","timestamp":1635883447000},"page":"1579-1601","source":"Crossref","is-referenced-by-count":6,"title":["Forecasting emergency department admissions"],"prefix":"10.1177","volume":"25","author":[{"given":"Carlos Narciso","family":"Rocha","sequence":"first","affiliation":[{"name":"ALERT Life Sciences Computing, Porto, Portugal"}]},{"given":"F\u00e1tima","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Interdisciplinary Studies Research Center, Institute of Engineering Polytechnic of Porto (ISEP\/IPP), Porto, Portugal"}]}],"member":"179","reference":[{"issue":"5","key":"10.3233\/IDA-205390_ref1","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1136\/emj.2010.103531","article-title":"Predicting emergency department admissions","volume":"29","author":"Boyle","year":"2012","journal-title":"Emerg Med J"},{"issue":"3","key":"10.3233\/IDA-205390_ref2","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1016\/j.dss.2012.12.019","article-title":"Modeling daily patient arrivals at Emergency Department and quantifying the relative importance of contributing variables using artificial neural network","volume":"54","author":"Xu","year":"2013","journal-title":"Decision Support Systems"},{"key":"10.3233\/IDA-205390_ref3","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.jbi.2015.06.022","article-title":"A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia","volume":"57","author":"Aboagye-Sarfo","year":"2015","journal-title":"Journal of Biomedical Informatics"},{"issue":"7","key":"10.3233\/IDA-205390_ref4","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10916-016-0527-0","article-title":"Forecasting the emergency department patients flow","volume":"40","author":"Afilal","year":"2016","journal-title":"Journal of Medical Systems"},{"issue":"4","key":"10.3233\/IDA-205390_ref5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.annemergmed.2014.10.008","article-title":"Forecasting emergency department visits using internet data","volume":"65","author":"Ekstr\u00f6m","year":"2015","journal-title":"Annals of Emergency Medicine"},{"issue":"2","key":"10.3233\/IDA-205390_ref6","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1097\/MEJ.0000000000000126","article-title":"Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology","volume":"22","author":"Handly","year":"2015","journal-title":"European Journal of Emergency Medicine"},{"key":"10.3233\/IDA-205390_ref7","doi-asserted-by":"crossref","unstructured":"M. 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