{"id":"https://openalex.org/W2774042070","doi":"https://doi.org/10.1109/healthcom.2017.8210801","title":"Short-term forecasting of hospital discharge volume based on time series analysis","display_name":"Short-term forecasting of hospital discharge volume based on time series analysis","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2774042070","doi":"https://doi.org/10.1109/healthcom.2017.8210801","mag":"2774042070"},"language":"en","primary_location":{"id":"doi:10.1109/healthcom.2017.8210801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom.2017.8210801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100642637","display_name":"Li Luo","orcid":"https://orcid.org/0000-0002-2007-7916"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Luo","raw_affiliation_strings":["Business school, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business school, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074031184","display_name":"Xueru Xu","orcid":"https://orcid.org/0000-0001-8797-3435"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueru Xu","raw_affiliation_strings":["Business school, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business school, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100649655","display_name":"Jialing Li","orcid":"https://orcid.org/0000-0003-1827-9313"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialing Li","raw_affiliation_strings":["Business school, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business school, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089462574","display_name":"Wen\u2010Wu Shen","orcid":"https://orcid.org/0000-0003-3038-4263"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210089761","display_name":"West China Hospital of Sichuan University","ror":"https://ror.org/007mrxy13","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210089761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwu Shen","raw_affiliation_strings":["The West China Hospital, Sichuan University, Chengdu, Sichuan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The West China Hospital, Sichuan University, Chengdu, Sichuan","institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210089761"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2687,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.66278996,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9571999907493591,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.917227029800415},{"id":"https://openalex.org/keywords/mean-absolute-percentage-error","display_name":"Mean absolute percentage error","score":0.761797308921814},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5573526620864868},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5550631284713745},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5095201134681702},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5050778985023499},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49441027641296387},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4935866594314575},{"id":"https://openalex.org/keywords/mean-absolute-error","display_name":"Mean absolute error","score":0.48337215185165405},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4823274314403534},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4562477171421051},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.42240530252456665},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.40514278411865234},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20670413970947266},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17389988899230957},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1526060700416565}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.917227029800415},{"id":"https://openalex.org/C150217764","wikidata":"https://www.wikidata.org/wiki/Q6803607","display_name":"Mean absolute percentage error","level":3,"score":0.761797308921814},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5573526620864868},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5550631284713745},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5095201134681702},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5050778985023499},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49441027641296387},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4935866594314575},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.48337215185165405},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4823274314403534},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4562477171421051},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.42240530252456665},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.40514278411865234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20670413970947266},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17389988899230957},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1526060700416565},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/healthcom.2017.8210801","is_oa":false,"landing_page_url":"https://doi.org/10.1109/healthcom.2017.8210801","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320336042","display_name":"West China Hospital, Sichuan University","ror":"https://ror.org/007mrxy13"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W196087439","https://openalex.org/W1229688857","https://openalex.org/W2016476642","https://openalex.org/W2032623688","https://openalex.org/W2055710504","https://openalex.org/W2117014758","https://openalex.org/W2339974927","https://openalex.org/W2342545017","https://openalex.org/W2582475167","https://openalex.org/W2588954203","https://openalex.org/W2615613308","https://openalex.org/W3007671429"],"related_works":["https://openalex.org/W2953369890","https://openalex.org/W2115811963","https://openalex.org/W4379469318","https://openalex.org/W2989181651","https://openalex.org/W74935964","https://openalex.org/W2056376122","https://openalex.org/W4388101383","https://openalex.org/W4292148067","https://openalex.org/W2796695317","https://openalex.org/W1767322088"],"abstract_inverted_index":{"The":[0,40,73,141],"hospital":[1,12,37,62],"bed":[2,38],"is":[3],"a":[4],"scarce":[5],"resource,":[6],"which":[7],"makes":[8],"it":[9],"difficult":[10],"for":[11,60],"administrators":[13],"to":[14,46,106,135,162,174],"manage.":[15],"Predicting":[16],"the":[17,32,49,61,68,79,82,137,146,151,157],"total":[18],"number":[19],"of":[20,36,42,53,81,139],"discharged":[21],"inpatients":[22],"(or":[23],"available":[24],"beds)":[25],"from":[26,78,87],"one":[27],"specific":[28],"department":[29],"can":[30,95,159],"improve":[31],"management":[33],"and":[34,65,120,128,156],"allocation":[35],"resources.":[39],"objectives":[41],"this":[43,100],"study":[44],"are":[45,133],"a)":[47],"predict":[48],"daily":[50],"inpatient":[51,168],"discharges":[52,74],"Nephrology":[54,80],"department,":[55],"b)":[56],"provide":[57],"decision":[58],"support":[59,96],"beds":[63,169],"manager":[64],"c)":[66],"find":[67],"most":[69],"appropriate":[70],"forecasting":[71],"model.":[72],"data":[75],"were":[76],"obtained":[77],"West":[83],"China":[84],"Hospital":[85],"(WCH)":[86],"2014":[88],"beds.":[89],"Data":[90],"mining":[91],"have":[92],"different":[93],"techniques":[94],"demand":[97],"prediction.":[98],"In":[99],"study,":[101],"we":[102],"choose":[103],"three":[104,152],"models":[105],"prediction,":[107],"respectively,":[108],"Autoregressive":[109],"Integrated":[110],"Moving":[111],"Average":[112],"Model":[113],"(ARIMA),":[114],"Long":[115],"Short-Term":[116],"Memory":[117],"model":[118,149],"(LSTM)":[119],"Random":[121],"Forests":[122],"(RF).":[123],"Normalized":[124],"mean":[125,129],"squared":[126],"error(NMSE)":[127],"absolute":[130],"percentage":[131],"error(MAPE)":[132],"utilized":[134],"assess":[136],"accuracy":[138],"results.":[140],"findings":[142],"indicate":[143],"that":[144],"RF,":[145],"only":[147],"multivariate":[148],"among":[150],"models,":[153],"performs":[154],"best,":[155],"results":[158],"be":[160],"used":[161],"aid":[163],"in":[164,172],"strategic":[165],"decision-making":[166],"on":[167],"resource":[170],"planning":[171],"response":[173],"predictable":[175],"discharges.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
