{"id":"https://openalex.org/W4293724321","doi":"https://doi.org/10.3390/info13090410","title":"Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach","display_name":"Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach","publication_year":2022,"publication_date":"2022-08-29","ids":{"openalex":"https://openalex.org/W4293724321","doi":"https://doi.org/10.3390/info13090410"},"language":"en","primary_location":{"id":"doi:10.3390/info13090410","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info13090410","pdf_url":"https://www.mdpi.com/2078-2489/13/9/410/pdf?version=1661773195","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2078-2489/13/9/410/pdf?version=1661773195","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055681932","display_name":"Huiquan Zhou","orcid":"https://orcid.org/0000-0003-1499-9735"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Huiquan Zhou","raw_affiliation_strings":["Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022833961","display_name":"Hao Luo","orcid":"https://orcid.org/0000-0003-4261-3414"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Hao Luo","raw_affiliation_strings":["Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Social Work and Social Administration, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012558485","display_name":"Kevin Ka\u2010Lun Lau","orcid":"https://orcid.org/0000-0003-3438-1182"},"institutions":[{"id":"https://openalex.org/I190632392","display_name":"Lule\u00e5 University of Technology","ror":"https://ror.org/016st3p78","country_code":"SE","type":"education","lineage":["https://openalex.org/I190632392"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Kevin Ka-Lun Lau","raw_affiliation_strings":["Department of Civil, Environmental and Natural Resources Engineering, Lule\u00e5 University of Technology, SE-97187 Lule\u00e5, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil, Environmental and Natural Resources Engineering, Lule\u00e5 University of Technology, SE-97187 Lule\u00e5, Sweden","institution_ids":["https://openalex.org/I190632392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021816159","display_name":"Xingxing Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xingxing Qian","raw_affiliation_strings":["School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633","https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044260589","display_name":"Chao Ren","orcid":"https://orcid.org/0000-0002-8494-2585"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chao Ren","raw_affiliation_strings":["Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong SAR, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050149941","display_name":"Pui Hing Chau","orcid":"https://orcid.org/0000-0003-0287-8020"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Puihing Chau","raw_affiliation_strings":["School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China"],"raw_orcid":"https://orcid.org/0000-0003-0287-8020","affiliations":[{"raw_affiliation_string":"School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China","institution_ids":["https://openalex.org/I177725633","https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5050149941"],"corresponding_institution_ids":["https://openalex.org/I177725633","https://openalex.org/I889458895"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.1486,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49864641,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"13","issue":"9","first_page":"410","last_page":"410"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10143","display_name":"Chronic Obstructive Pulmonary Disease (COPD) Research","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10143","display_name":"Chronic Obstructive Pulmonary Disease (COPD) Research","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11244","display_name":"Climate Change and Health Impacts","score":0.9839000105857849,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.9771000146865845,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6964472532272339},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6816658973693848},{"id":"https://openalex.org/keywords/emergency-department","display_name":"Emergency department","score":0.6587093472480774},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6057692170143127},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5852099657058716},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.52439945936203},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5019972324371338},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.464956134557724},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4574716091156006},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4267321527004242},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4239109456539154},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41646796464920044},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.31479310989379883},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22067946195602417},{"id":"https://openalex.org/keywords/environmental-health","display_name":"Environmental health","score":0.08443614840507507}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6964472532272339},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6816658973693848},{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.6587093472480774},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6057692170143127},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5852099657058716},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.52439945936203},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5019972324371338},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.464956134557724},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4574716091156006},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4267321527004242},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4239109456539154},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41646796464920044},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.31479310989379883},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22067946195602417},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.08443614840507507},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/info13090410","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info13090410","pdf_url":"https://www.mdpi.com/2078-2489/13/9/410/pdf?version=1661773195","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a4352447a18d4945a49ab764b2a3e114","is_oa":true,"landing_page_url":"https://doaj.org/article/a4352447a18d4945a49ab764b2a3e114","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information, Vol 13, Iss 9, p 410 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2078-2489/13/9/410/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/info13090410","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Information; Volume 13; Issue 9; Pages: 410","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/info13090410","is_oa":true,"landing_page_url":"https://doi.org/10.3390/info13090410","pdf_url":"https://www.mdpi.com/2078-2489/13/9/410/pdf?version=1661773195","source":{"id":"https://openalex.org/S4210219776","display_name":"Information","issn_l":"2078-2489","issn":["2078-2489"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4293724321.pdf","grobid_xml":"https://content.openalex.org/works/W4293724321.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W38301456","https://openalex.org/W75231382","https://openalex.org/W219012541","https://openalex.org/W1968193277","https://openalex.org/W1969298821","https://openalex.org/W1969886081","https://openalex.org/W1970414061","https://openalex.org/W1997475212","https://openalex.org/W2028711237","https://openalex.org/W2047452988","https://openalex.org/W2060557082","https://openalex.org/W2100293965","https://openalex.org/W2101234009","https://openalex.org/W2142827986","https://openalex.org/W2157331557","https://openalex.org/W2285052861","https://openalex.org/W2523317561","https://openalex.org/W2565516711","https://openalex.org/W2605920200","https://openalex.org/W2608348062","https://openalex.org/W2736845296","https://openalex.org/W2781767546","https://openalex.org/W2885204485","https://openalex.org/W2895973372","https://openalex.org/W2901424910","https://openalex.org/W2946239178","https://openalex.org/W2955315361","https://openalex.org/W3011471929","https://openalex.org/W3020607320","https://openalex.org/W3024862500","https://openalex.org/W3080990858","https://openalex.org/W3084378266","https://openalex.org/W3119285570","https://openalex.org/W3126003412","https://openalex.org/W3159255389","https://openalex.org/W3160076253","https://openalex.org/W3171661082","https://openalex.org/W3173467144","https://openalex.org/W4288077575","https://openalex.org/W4295312788","https://openalex.org/W6601578796","https://openalex.org/W6675354045","https://openalex.org/W6766978945","https://openalex.org/W6840954249"],"related_works":["https://openalex.org/W4384345078","https://openalex.org/W2791973335","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W4313289487","https://openalex.org/W3191198889","https://openalex.org/W4399767560","https://openalex.org/W4399653172"],"abstract_inverted_index":{"Previous":[0],"evidence":[1],"suggests":[2],"that":[3],"temperature":[4,24,49,125,171],"is":[5,27],"associated":[6],"with":[7,43],"the":[8,35,122,142,160,165,188,192,200],"number":[9],"of":[10,38,82,93,106,113],"emergency":[11],"department":[12],"(ED)":[13],"visits.":[14],"A":[15,80],"predictive":[16,36,58,124,194],"system":[17],"for":[18,60,164],"ED":[19,55,65,94],"visits,":[20],"which":[21],"takes":[22],"local":[23],"into":[25],"account,":[26],"therefore":[28],"needed.":[29],"This":[30],"study":[31],"aimed":[32],"to":[33,74,78,90,120],"compare":[34],"performance":[37],"various":[39],"machine":[40,132,205],"learning":[41,197,206],"methods":[42,46,207],"traditional":[44,209],"statistical":[45,210],"based":[47],"on":[48,141,159],"variables":[50],"and":[51,97,99,103,109,111,135,151,208],"develop":[52],"a":[53,175],"daily":[54],"attendance":[56,95],"rate":[57,96],"model":[59],"Hong":[61,68],"Kong.":[62],"We":[63],"analyzed":[64],"utilization":[66],"among":[67],"Kong":[69],"older":[70],"adults":[71],"in":[72,178],"May":[73],"September":[75],"from":[76,88],"2000":[77],"2016.":[79],"total":[81],"103":[83],"potential":[84],"predictors":[85],"were":[86,139,157],"derived":[87],"1-":[89],"14-day":[91,167],"lag":[92,105],"meteorological":[98],"air":[100],"quality":[101],"indicators":[102],"0-day":[104],"holiday":[107],"indicator":[108,185],"month":[110],"day":[112],"week":[114],"indicators.":[115],"LASSO":[116],"regression":[117],"was":[118,172],"used":[119],"identify":[121],"most":[123],"variables.":[126],"Decision":[127],"tree":[128],"regressor,":[129,134],"support":[130],"vector":[131],"(SVM)":[133],"random":[136],"forest":[137],"regressor":[138],"trained":[140],"selected":[143],"optimal":[144],"predictor":[145,162,177],"combination.":[146],"Deep":[147,196],"neural":[148],"network":[149],"(DNN)":[150],"gated":[152],"recurrent":[153],"unit":[154],"(GRU)":[155],"models":[156],"performed":[158],"extended":[161],"combination":[163],"previous":[166],"horizon.":[168],"Maximum":[169],"ambient":[170],"identified":[173],"as":[174,183],"better":[176],"its":[179],"own":[180],"value":[181],"than":[182],"an":[184],"defined":[186],"by":[187],"cutoff.":[189],"GRU":[190,201],"achieved":[191],"best":[193],"accuracy.":[195],"methods,":[198],"especially":[199],"model,":[202],"outperformed":[203],"conventional":[204],"methods.":[211]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}