{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T18:48:54Z","timestamp":1735584534155},"reference-count":30,"publisher":"Emerald","issue":"9","license":[{"start":{"date-parts":[[2019,10,16]],"date-time":"2019-10-16T00:00:00Z","timestamp":1571184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2019,10,16]]},"abstract":"\nPurpose<\/jats:title>\nThis study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven\u2009days.<\/jats:p>\n<\/jats:sec>\n\nDesign\/methodology\/approach<\/jats:title>\nIn this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.<\/jats:p>\n<\/jats:sec>\n\nFindings<\/jats:title>\nAlthough many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.<\/jats:p>\n<\/jats:sec>\n\nResearch limitations\/implications<\/jats:title>\nThe authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.<\/jats:p>\n<\/jats:sec>\n\nOriginality\/value<\/jats:title>\nTo the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-10-2018-0520","type":"journal-article","created":{"date-parts":[[2019,10,28]],"date-time":"2019-10-28T08:52:27Z","timestamp":1572252747000},"page":"2335-2348","source":"Crossref","is-referenced-by-count":24,"title":["Patient visit forecasting in an emergency department using a deep neural network approach"],"prefix":"10.1108","volume":"49","author":[{"given":"Milad","family":"Yousefi","sequence":"first","affiliation":[]},{"given":"Moslem","family":"Yousefi","sequence":"additional","affiliation":[]},{"given":"Masood","family":"Fathi","sequence":"additional","affiliation":[]},{"given":"Flavio S.","family":"Fogliatto","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"3","key":"key2020090405303170400_ref001","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TITB.2009.2014565","article-title":"Short-term forecasting of emergency inpatient flow","volume":"13","year":"2009","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"issue":"7","key":"key2020090405303170400_ref002","first-page":"1","article-title":"A deep learning framework for financial time series using stacked autoencoders and long- short term memory","volume":"12","year":"2017","journal-title":"PloS One"},{"issue":"2","key":"key2020090405303170400_ref003","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long term dependencies with gradient descent is difficult","volume":"5","year":"1994","journal-title":"IEEE Transactions on Neural Networks"},{"key":"key2020090405303170400_ref004","article-title":"Forecasting daily volume and acuity of patients in the emergency department","volume":"2016","year":"2016","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"5","key":"key2020090405303170400_ref005","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1136\/bjsm.33.5.333","article-title":"A major sporting event does not necessarily mean an increased workload for accident and emergency departments","volume":"33","year":"1999","journal-title":"British Journal of Sports Medicine"},{"issue":"4","key":"key2020090405303170400_ref006","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","year":"2015","journal-title":"Annals of Emergency Medicine"},{"key":"key2020090405303170400_ref007","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1016\/j.jhydrol.2015.09.028","article-title":"Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers","volume":"529","year":"2015","journal-title":"Journal of Hydrology"},{"key":"key2020090405303170400_ref008","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.ijpe.2016.09.020","article-title":"Predicting hospital admissions to reduce emergency department boarding","volume":"182","year":"2016","journal-title":"International Journal of Production Economics"},{"issue":"2","key":"key2020090405303170400_ref009","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1111\/j.1532-5415.2005.00587.x","article-title":"The effect of emergency department crowding on the management of pain in older adults with hip fracture","volume":"54","year":"2006","journal-title":"Journal of the American Geriatrics Society"},{"key":"key2020090405303170400_ref010","volume-title":"Scholar. Hospital-Based Emergency Care: At the Breaking Point","author":"Institute of Medicine Committee on the Future of Emergency Care in the US Health System","year":"2006"},{"issue":"2","key":"key2020090405303170400_ref011","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1111\/j.1553-2712.2007.00032.x","article-title":"Forecasting daily patient volumes in the emergency department","volume":"15","year":"2008","journal-title":"Academic Emergency Medicine"},{"issue":"3","key":"key2020090405303170400_ref012","doi-asserted-by":"crossref","first-page":"158","DOI":"10.4258\/hir.2010.16.3.158","article-title":"Prediction of daily patient numbers for a regional emergency medical center using time series analysis","volume":"16","year":"2010","journal-title":"Healthcare Informatics Research"},{"issue":"1","key":"key2020090405303170400_ref013","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1071\/AH09866","article-title":"Increasing utilisation of emergency ambulances","volume":"35","year":"2011","journal-title":"Australian Health Review"},{"issue":"6","key":"key2020090405303170400_ref014","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.annemergmed.2006.05.001","article-title":"Ambulance diversion and lost hospital revenues","volume":"48","year":"2006","journal-title":"Annals of Emergency Medicine"},{"issue":"8","key":"key2020090405303170400_ref015","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1111\/acem.12182","article-title":"Forecasting daily emergency department visits using calendar variables and ambient temperature readings","volume":"20","year":"2013","journal-title":"Academic Emergency Medicine"},{"key":"key2020090405303170400_ref016","first-page":"1045","article-title":"Recurrent neural network based language model","year":"2015"},{"issue":"4","key":"key2020090405303170400_ref017","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1017\/S1049023X00001291","article-title":"Variables influencing medical usage rates, injury patterns, and levels of care for mass gatherings","volume":"18","year":"2003","journal-title":"Prehospital and Disaster Medicine"},{"issue":"3","key":"key2020090405303170400_ref018","first-page":"277","article-title":"Emergency department overcrowding and ambulance transport delays for patients with chest pain","volume":"168","year":"2003","journal-title":"Cmaj"},{"issue":"8","key":"key2020090405303170400_ref019","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term memory","volume":"9","year":"1997","journal-title":"Neural Computation"},{"issue":"1","key":"key2020090405303170400_ref020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-227X-9-1","article-title":"Forecasting daily attendances at an emergency department to aid resource planning","volume":"9","year":"2009","journal-title":"BMC Emergency Medicine"},{"issue":"3","key":"key2020090405303170400_ref021","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1080\/14697680903426573","article-title":"A comparison of statistical tests for the adequacy of a neural network regression model","volume":"12","year":"2012","journal-title":"Quantitative Finance"},{"issue":"6","key":"key2020090405303170400_ref022","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1136\/emj.2008.062380","article-title":"A systematic review of models for forecasting the number of emergency department visits","volume":"26","year":"2009","journal-title":"Emergency Medicine Journal"},{"issue":"3","key":"key2020090405303170400_ref023","doi-asserted-by":"crossref","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","year":"2013","journal-title":"Decision Support Systems"},{"issue":"8","key":"key2020090405303170400_ref024","doi-asserted-by":"crossref","first-page":"2751","DOI":"10.1002\/qre.2095","article-title":"A hybrid approach for forecasting patient visits in emergency department","volume":"32","year":"2016","journal-title":"Quality and Reliability Engineering International"},{"issue":"5","key":"key2020090405303170400_ref025","article-title":"An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department","volume":"50","year":"2017","journal-title":"Brazilian Journal of Medical and Biological Research = Research"},{"key":"key2020090405303170400_ref026","article-title":"Human resource allocation in an emergency department","year":"2019","journal-title":"Kybernetes"},{"key":"key2020090405303170400_ref027","article-title":"An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: a preliminary case study","year":"2016"},{"key":"key2020090405303170400_ref028","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.artmed.2017.10.002","article-title":"Chaotic genetic algorithm and AdaBoost ensemble metamodeling approach for optimum resource planning in emergency departments","volume":"84","year":"2018","journal-title":"Artificial Intelligence in Medicine"},{"issue":"3","key":"key2020090405303170400_ref029","first-page":"1","article-title":"Simulating the behavior of patients who leave a public hospital emergency department without being seen by a physician: a cellular automaton and agent-based framework","volume":"51","year":"2018","journal-title":"Brazilian Journal of Medical and Biological Research"},{"key":"key2020090405303170400_ref030","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.envpol.2017.02.010","article-title":"Ambient temperature and emergency department visits: time-series analysis in 12 Chinese cities","volume":"224","year":"2017","journal-title":"Environmental Pollution"}],"container-title":["Kybernetes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-10-2018-0520\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-10-2018-0520\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T05:31:45Z","timestamp":1599197505000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/K-10-2018-0520\/full\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,16]]},"references-count":30,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2019,10,16]]}},"alternative-id":["10.1108\/K-10-2018-0520"],"URL":"https:\/\/doi.org\/10.1108\/k-10-2018-0520","relation":{},"ISSN":["0368-492X","0368-492X"],"issn-type":[{"value":"0368-492X","type":"print"},{"value":"0368-492X","type":"print"}],"subject":[],"published":{"date-parts":[[2019,10,16]]}}}