{"id":"https://openalex.org/W2788608859","doi":"https://doi.org/10.1109/access.2018.2808843","title":"Using Data Mining to Predict Hospital Admissions From the Emergency Department","display_name":"Using Data Mining to Predict Hospital Admissions From the Emergency Department","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2788608859","doi":"https://doi.org/10.1109/access.2018.2808843","mag":"2788608859"},"language":"en","primary_location":{"id":"doi:10.1109/access.2018.2808843","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2808843","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2018.2808843","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070535926","display_name":"Byron Graham","orcid":"https://orcid.org/0000-0002-8121-3520"},"institutions":[{"id":"https://openalex.org/I126231945","display_name":"Queen's University Belfast","ror":"https://ror.org/00hswnk62","country_code":"GB","type":"education","lineage":["https://openalex.org/I126231945"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Byron Graham","raw_affiliation_strings":["Management School, Queen\u2019s University Belfast, Belfast, U.K","Management School, Queen's University Belfast, Belfast, U.K"],"raw_orcid":"https://orcid.org/0000-0002-8121-3520","affiliations":[{"raw_affiliation_string":"Management School, Queen\u2019s University Belfast, Belfast, U.K","institution_ids":["https://openalex.org/I126231945"]},{"raw_affiliation_string":"Management School, Queen's University Belfast, Belfast, U.K","institution_ids":["https://openalex.org/I126231945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067279312","display_name":"Raymond Bond","orcid":"https://orcid.org/0000-0002-1078-2232"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Raymond Bond","raw_affiliation_strings":["School of Computing and Mathematics, Ulster University, Newtownabbey, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematics, Ulster University, Newtownabbey, U.K","institution_ids":["https://openalex.org/I138801177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044583406","display_name":"Michael Quinn","orcid":"https://orcid.org/0000-0003-0694-3870"},"institutions":[{"id":"https://openalex.org/I32375777","display_name":"Northern Health and Social Care Trust","ror":"https://ror.org/01bgbk171","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I32375777"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Michael Quinn","raw_affiliation_strings":["Northern Health and Social Care Trust, Antrim, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northern Health and Social Care Trust, Antrim, U.K","institution_ids":["https://openalex.org/I32375777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038610271","display_name":"Maurice Mulvenna","orcid":"https://orcid.org/0000-0002-1554-0785"},"institutions":[{"id":"https://openalex.org/I138801177","display_name":"University of Ulster","ror":"https://ror.org/01yp9g959","country_code":"GB","type":"education","lineage":["https://openalex.org/I138801177"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Maurice Mulvenna","raw_affiliation_strings":["School of Computing and Mathematics, Ulster University, Newtownabbey, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematics, Ulster University, Newtownabbey, U.K","institution_ids":["https://openalex.org/I138801177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":19.2586,"has_fulltext":true,"cited_by_count":145,"citation_normalized_percentile":{"value":0.99583895,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"6","issue":null,"first_page":"10458","last_page":"10469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":1.0,"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"}},{"id":"https://openalex.org/T12246","display_name":"Chronic Disease Management Strategies","score":0.9526000022888184,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T10391","display_name":"Healthcare Policy and Management","score":0.9501000046730042,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7954938411712646},{"id":"https://openalex.org/keywords/triage","display_name":"Triage","score":0.7394751310348511},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7210478186607361},{"id":"https://openalex.org/keywords/overcrowding","display_name":"Overcrowding","score":0.6680102944374084},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6506361365318298},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6305220723152161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5874795317649841},{"id":"https://openalex.org/keywords/emergency-department","display_name":"Emergency department","score":0.5801230072975159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5305932760238647},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4673517048358917},{"id":"https://openalex.org/keywords/emergency-medicine","display_name":"Emergency medicine","score":0.4471658170223236},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.41018348932266235},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40075716376304626}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7954938411712646},{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.7394751310348511},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7210478186607361},{"id":"https://openalex.org/C2778872837","wikidata":"https://www.wikidata.org/wiki/Q7113614","display_name":"Overcrowding","level":2,"score":0.6680102944374084},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6506361365318298},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6305220723152161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5874795317649841},{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.5801230072975159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5305932760238647},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4673517048358917},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.4471658170223236},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.41018348932266235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40075716376304626},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/access.2018.2808843","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2808843","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:pure.qub.ac.uk/portal:publications/fe64c1ed-17c1-4995-8009-2346e65aa46e","is_oa":true,"landing_page_url":"https://pure.qub.ac.uk/en/publications/fe64c1ed-17c1-4995-8009-2346e65aa46e","pdf_url":"https://pureadmin.qub.ac.uk/ws/files/147123726/DComp.pdf","source":{"id":"https://openalex.org/S4306402319","display_name":"Research Portal (Queen's University Belfast)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126231945","host_organization_name":"Queen's University Belfast","host_organization_lineage":["https://openalex.org/I126231945"],"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":"Graham , B , Bond , R , Quinn , M & Mulvenna , M 2018 , ' Using Data Mining to Predict Hospital Admissions from the Emergency Department ' , IEEE Access , vol. 6 , no. 1 , pp. 10458-10469 . https://doi.org/10.1109/ACCESS.2018.2808843","raw_type":"article"},{"id":"pmh:oai:doaj.org/article:b2e419f46d854dba9e3368dd6679314f","is_oa":true,"landing_page_url":"https://doaj.org/article/b2e419f46d854dba9e3368dd6679314f","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":"IEEE Access, Vol 6, Pp 10458-10469 (2018)","raw_type":"article"},{"id":"pmh:oai:pure.atira.dk:openaire/ad121988-1253-430c-ad2f-105b865aaaf3","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/ad121988-1253-430c-ad2f-105b865aaaf3","pdf_url":null,"source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Graham, B, Bond, R, Quinn, M & Mulvenna, M 2018, 'Using Data Mining to Predict Hospital Admissions from the Emergency Department', IEEE Access, vol. 6, pp. 10458-10469. https://doi.org/10.1109/ACCESS.2018.2808843","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.atira.dk:publications/ad121988-1253-430c-ad2f-105b865aaaf3","is_oa":true,"landing_page_url":"https://pure.ulster.ac.uk/en/publications/using-data-mining-to-predict-hospital-admissions-from-the-emergen","pdf_url":null,"source":{"id":"https://openalex.org/S4306402454","display_name":"Ulster University Research Portal (Ulster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I138801177","host_organization_name":"University of Ulster","host_organization_lineage":["https://openalex.org/I138801177"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Graham, B, Bond, R, Quinn, M & Mulvenna, M 2018, 'Using Data Mining to Predict Hospital Admissions from the Emergency Department', IEEE Access, vol. 6, pp. 10458-10469. https://doi.org/10.1109/ACCESS.2018.2808843","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1109/access.2018.2808843","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2018.2808843","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.5299999713897705}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W24225093","https://openalex.org/W77686088","https://openalex.org/W125064766","https://openalex.org/W429766147","https://openalex.org/W1483994939","https://openalex.org/W1491002046","https://openalex.org/W1562588319","https://openalex.org/W1594031697","https://openalex.org/W1823994478","https://openalex.org/W1887152358","https://openalex.org/W1966685894","https://openalex.org/W1973948212","https://openalex.org/W1975152552","https://openalex.org/W1979425944","https://openalex.org/W1979528456","https://openalex.org/W1992803797","https://openalex.org/W1998399571","https://openalex.org/W1999794141","https://openalex.org/W2000063416","https://openalex.org/W2009167374","https://openalex.org/W2044286296","https://openalex.org/W2056793409","https://openalex.org/W2065146078","https://openalex.org/W2077061001","https://openalex.org/W2078242043","https://openalex.org/W2085791174","https://openalex.org/W2087264237","https://openalex.org/W2091337766","https://openalex.org/W2095436423","https://openalex.org/W2096367109","https://openalex.org/W2096742836","https://openalex.org/W2124799218","https://openalex.org/W2143331144","https://openalex.org/W2143649626","https://openalex.org/W2146767951","https://openalex.org/W2150179549","https://openalex.org/W2161123687","https://openalex.org/W2162623754","https://openalex.org/W2254232899","https://openalex.org/W2568818165","https://openalex.org/W2993698338","https://openalex.org/W3083620949","https://openalex.org/W3124326471","https://openalex.org/W3145666912","https://openalex.org/W4256436395","https://openalex.org/W4299689471","https://openalex.org/W6600025118"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4321054264","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W1515008438","https://openalex.org/W3154586736","https://openalex.org/W4293151273"],"abstract_inverted_index":{"Crowding":[0],"within":[1],"emergency":[2],"departments":[3],"(EDs)":[4],"can":[5],"have":[6],"significant":[7],"negative":[8],"consequences":[9],"for":[10,210],"patients.":[11],"EDs":[12,237],"therefore":[13],"need":[14],"to":[15,22,42,64,83,140],"explore":[16],"the":[17,33,72,77,85,111,121,156,163,169,185,203,215],"use":[18,34,80],"of":[19,35,74,172,184,199,225],"innovative":[20],"methods":[21],"improve":[23],"patient":[24,180,219],"flow":[25],"and":[26,94,120,159,214,227],"prevent":[27],"overcrowding.":[28],"One":[29],"potential":[30,170],"method":[31],"is":[32,233,251],"data":[36,52],"mining":[37],"using":[38],"machine":[39,67,175],"learning":[40,68,176],"techniques":[41],"predict":[43],"ED":[44,204],"admissions.":[45,181],"This":[46,166],"paper":[47,167,190],"uses":[48],"routinely":[49],"collected":[50],"administrative":[51],"(120":[53],"600":[54],"records)":[55],"from":[56,76,202],"two":[57],"major":[58],"acute":[59],"hospitals":[60],"in":[61,70,155,162,178,188,191,218],"Northern":[62],"Ireland":[63],"compare":[65],"contrasting":[66],"algorithms":[69,82,177],"predicting":[71,179],"risk":[73],"admission":[75,154,161,229],"ED.":[78],"We":[79],"three":[81,173],"build":[84],"predictive":[86],"models:":[87],"1)":[88],"logistic":[89,122,133,241],"regression;":[90],"2)":[91],"decision":[92,112,192],"trees;":[93],"3)":[95],"gradient":[96],"boosted":[97],"machines":[98],"(GBM).":[99],"The":[100],"GBM":[101],"performed":[102],"better":[103],"(accuracy":[104,114,125],"=":[105,108,115,118,126,129],"80.31%,":[106],"AUC-ROC":[107,117,128],"0.859)":[109],"than":[110],"tree":[113],"80.06%,":[116],"0.824)":[119],"regression":[123,242],"model":[124],"79.94%,":[127],"0.849).":[130],"Drawing":[131],"on":[132],"regression,":[134],"we":[135],"identify":[136],"several":[137],"factors":[138],"related":[139],"hospital":[141,144],"admissions,":[142],"including":[143],"site,":[145],"age,":[146],"arrival":[147],"mode,":[148],"triage":[149],"category,":[150],"care":[151],"group,":[152],"previous":[153,160],"past":[157,164],"month,":[158],"year.":[165],"highlights":[168],"utility":[171],"common":[174],"Practical":[182],"implementation":[183],"models":[186],"developed":[187],"this":[189],"support":[193],"tools":[194],"would":[195],"provide":[196],"a":[197,206,234],"snapshot":[198],"predicted":[200,226],"admissions":[201],"at":[205],"given":[207],"time,":[208],"allowing":[209],"advance":[211],"resource":[212],"planning":[213],"avoidance":[216],"bottlenecks":[217],"flow,":[220],"as":[221,223],"well":[222],"comparison":[224],"actual":[228],"rates.":[230],"When":[231],"interpretability":[232],"key":[235],"consideration,":[236],"should":[238],"consider":[239],"adopting":[240],"models,":[243],"although":[244],"GBM\u2019s":[245],"will":[246],"be":[247],"useful":[248],"where":[249],"accuracy":[250],"paramount.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":24},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":33},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}