{"id":"https://openalex.org/W2199768047","doi":"https://doi.org/10.1109/bibm.2015.7359790","title":"Imbalanced learning to predict long stay Emergency Department patients","display_name":"Imbalanced learning to predict long stay Emergency Department patients","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2199768047","doi":"https://doi.org/10.1109/bibm.2015.7359790","mag":"2199768047"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2015.7359790","doi":"https://doi.org/10.1109/bibm.2015.7359790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359790","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5075189110","display_name":"Ali Azari","orcid":"https://orcid.org/0000-0003-0953-9523","observed_orcids":["https://orcid.org/0000-0003-0953-9523"]},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Azari","raw_affiliation_strings":["Department of Information System, University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information System, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063541070","display_name":"Vandana Pursnani Janeja","orcid":"https://orcid.org/0000-0003-0130-6135","observed_orcids":["https://orcid.org/0000-0003-0130-6135"]},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vandana P. Janeja","raw_affiliation_strings":["Department of Information System, University of Maryland, Baltimore County, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information System, University of Maryland, Baltimore County, Baltimore, MD, USA","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103033927","display_name":"Scott Levin","orcid":"https://orcid.org/0000-0002-7143-7635","observed_orcids":["https://orcid.org/0000-0002-7143-7635"]},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Scott Levin","raw_affiliation_strings":["Emergency Medicine Johns Hopkins University, Baltimore, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Emergency Medicine Johns Hopkins University, Baltimore, MD, USA","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.3273,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.95894428,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"807","last_page":"814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.79830002784729,"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":0.79830002784729,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.09040000289678574,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.04650000110268593,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/emergency-department-crowding","display_name":"emergency department overcrowding","score":0.9449999928474426},{"id":"https://openalex.org/keywords/length-of-stay-prediction","display_name":"length of stay prediction","score":0.9210000038146973},{"id":"https://openalex.org/keywords/imbalanced-data-classification","display_name":"imbalanced classification","score":0.9340000152587891},{"id":"https://openalex.org/keywords/minority-class-prediction","display_name":"minority class prediction","score":0.6949999928474426},{"id":"https://openalex.org/keywords/triage","display_name":"triage","score":0.7269999980926514},{"id":"https://openalex.org/keywords/ensemble-methods","display_name":"ensemble methods","score":0.6050000190734863},{"id":"https://openalex.org/keywords/interpretable-prediction","display_name":"interpretable prediction","score":0.44999998807907104},{"id":"https://openalex.org/keywords/hospital-bed-utilization","display_name":"hospital bed utilization","score":0.5260000228881836}],"concepts":[{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.8692218065261841},{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.8120890855789185},{"id":"https://openalex.org/C149333683","wikidata":"https://www.wikidata.org/wiki/Q5189188","display_name":"Crowding","level":2,"score":0.6956335306167603},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.501929521560669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49907588958740234},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.49832749366760254},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47258293628692627},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.46982812881469727},{"id":"https://openalex.org/C22684755","wikidata":"https://www.wikidata.org/wiki/Q847526","display_name":"Queueing theory","level":2,"score":0.45125070214271545},{"id":"https://openalex.org/C2777601897","wikidata":"https://www.wikidata.org/wiki/Q3409113","display_name":"Presentation (obstetrics)","level":2,"score":0.42395007610321045},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.3480491638183594},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.32324305176734924},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1556967794895172},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.12179809808731079},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.11035138368606567},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.09103450179100037},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2015.7359790","doi":"https://doi.org/10.1109/bibm.2015.7359790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359790","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8371999859809875}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310145","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1124400689","https://openalex.org/W1485256945","https://openalex.org/W1594794537","https://openalex.org/W1608372367","https://openalex.org/W1985629909","https://openalex.org/W2007281290","https://openalex.org/W2024223694","https://openalex.org/W2038804786","https://openalex.org/W2047052762","https://openalex.org/W2054091417","https://openalex.org/W2062421497","https://openalex.org/W2096945460","https://openalex.org/W2099454382","https://openalex.org/W2102267249","https://openalex.org/W2103614420","https://openalex.org/W2127082287","https://openalex.org/W2133992814","https://openalex.org/W2136256517","https://openalex.org/W2148143831","https://openalex.org/W2237451895","https://openalex.org/W2591868163","https://openalex.org/W2900381886","https://openalex.org/W4212883601","https://openalex.org/W4230785767","https://openalex.org/W6734298239"],"related_works":["https://openalex.org/W3021103870","https://openalex.org/W4401821474","https://openalex.org/W4407386597","https://openalex.org/W4406819447","https://openalex.org/W3003580243","https://openalex.org/W4390665255","https://openalex.org/W4393258421","https://openalex.org/W3170241702","https://openalex.org/W4385350574","https://openalex.org/W2885604873"],"abstract_inverted_index":{"A":[0,61],"major":[1],"contributor":[2],"to":[3,50,82,108],"Emergency":[4],"Department":[5],"(ED)":[6],"crowding":[7],"is":[8,90],"patients":[9,134],"with":[10,17,22,135,178],"prolonged":[11,69,136,224,259],"length":[12,226],"of":[13,31,37,104,154,162,181,227,234],"stay":[14,70,228],"(LOS).":[15],"Patients":[16],"long":[18],"stays":[19,138,260],"(i.e.,":[20,147],"those":[21],"LOS":[23,46],"longer":[24],"than":[25,261],"14":[26,140],"hours)":[27,141],"comprise":[28],"10%":[29],"percent":[30],"ED":[32,40,57,86,137,188,208,225],"visits,":[33],"but":[34],"utilize":[35],"30%":[36],"the":[38,56,59,101,110,159,163,166,187,193,235],"total":[39],"bed":[41],"hours.":[42],"Accurately":[43],"predicting":[44],"patients'":[45],"can":[47,65,116,190],"be":[48,77,117],"used":[49],"improve":[51],"resource":[52],"management":[53],"both":[54],"in":[55,79,165,186,198],"and":[58,85,106,175,183,229],"hospital.":[60,215],"prediction":[62,196],"model":[63],"that":[64,132,157,220,237,243],"identify":[66],"this":[67,89,126],"minority,":[68],"patient":[71,170],"group,":[72],"early":[73],"at":[74,145,212],"presentation":[75],"may":[76],"effective":[78],"addressing":[80],"barriers":[81],"expedited":[83],"treatment":[84],"disposition.":[87],"However,":[88],"a":[91,130,179,231,245],"challenging":[92],"task":[93],"because":[94],"regular":[95],"classification":[96],"techniques":[97],"are":[98,201],"biased":[99],"toward":[100],"majority":[102],"group":[103],"examples":[105],"tend":[107],"overlook":[109],"minority":[111],"class":[112,121,246],"examples.":[113],"This":[114],"problem":[115],"alleviated":[118],"by":[119],"using":[120,263],"imbalance":[122,247],"learning":[123,248],"methods.":[124,267],"In":[125],"paper,":[127],"we":[128,221],"present":[129],"framework":[131,150,200,253],"predicts":[133],"(>":[139],"from":[142,203],"data":[143],"available":[144],"triage":[146],"presentation).":[148],"The":[149,195],"also":[151,241],"enables":[152],"extraction":[153],"independent":[155],"variables":[156],"capture":[158],"current":[160],"state":[161],"resources":[164,182],"ED.":[167],"Predictions":[168],"combine":[169],"information":[171],"(e.g.,":[172],"demographics,":[173],"complaints,":[174],"vital":[176],"signs)":[177],"snapshot":[180],"queuing":[184],"metrics":[185],"which":[189],"substantially":[191],"impact":[192],"LOS.":[194],"models":[197],"our":[199,252],"developed":[202],"over":[204],"one":[205],"hundred":[206],"thousand":[207],"encounters":[209],"retrospectively":[210],"collected":[211],"an":[213],"urban":[214],"Our":[216],"experimental":[217],"results":[218,257],"demonstrate":[219],"accurately":[222],"predict":[223],"provide":[230],"clear":[232],"interpretation":[233],"factors":[236],"influence":[238],"it.":[239],"We":[240],"found":[242],"integrating":[244],"ensemble":[249],"method":[250],"into":[251],"produces":[254],"much":[255],"better":[256],"for":[258],"only":[262],"traditional":[264],"logistic":[265],"regression":[266]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}