{"id":"https://openalex.org/W2557741588","doi":"https://doi.org/10.1145/2975167.2985845","title":"Predicting Future Frequent Users of Emergency Departments in California State","display_name":"Predicting Future Frequent Users of Emergency Departments in California State","publication_year":2016,"publication_date":"2016-10-02","ids":{"openalex":"https://openalex.org/W2557741588","doi":"https://doi.org/10.1145/2975167.2985845","mag":"2557741588"},"language":"en","primary_location":{"id":"doi:10.1145/2975167.2985845","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2975167.2985845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","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/A5014759332","display_name":"Mayana Pereira","orcid":"https://orcid.org/0000-0001-8636-8882"},"institutions":[{"id":"https://openalex.org/I4210150356","display_name":"University of Washington Tacoma","ror":"https://ror.org/05n8t2628","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210150356"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mayana Pereira","raw_affiliation_strings":["Center for Data Science, Institute of Technology, University of Washington, Tacoma"],"affiliations":[{"raw_affiliation_string":"Center for Data Science, Institute of Technology, University of Washington, Tacoma","institution_ids":["https://openalex.org/I4210150356"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043254796","display_name":"Vikhyati Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210150356","display_name":"University of Washington Tacoma","ror":"https://ror.org/05n8t2628","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210150356"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vikhyati Singh","raw_affiliation_strings":["Center for Data Science, Institute of Technology, University of Washington, Tacoma"],"affiliations":[{"raw_affiliation_string":"Center for Data Science, Institute of Technology, University of Washington, Tacoma","institution_ids":["https://openalex.org/I4210150356"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091357963","display_name":"Chun Pan Hon","orcid":null},"institutions":[{"id":"https://openalex.org/I4210150356","display_name":"University of Washington Tacoma","ror":"https://ror.org/05n8t2628","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210150356"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chun Pan Hon","raw_affiliation_strings":["Center for Data Science, Institute of Technology, University of Washington, Tacoma"],"affiliations":[{"raw_affiliation_string":"Center for Data Science, Institute of Technology, University of Washington, Tacoma","institution_ids":["https://openalex.org/I4210150356"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102757300","display_name":"Tomas McKelvey","orcid":"https://orcid.org/0000-0001-8552-3385"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"T. Greg McKelvey","raw_affiliation_strings":["KenSci, Seattle and Occupational &amp; Environmental Medicine Fellow University of Washington"],"affiliations":[{"raw_affiliation_string":"KenSci, Seattle and Occupational &amp; Environmental Medicine Fellow University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044869726","display_name":"Shanu Sushmita","orcid":null},"institutions":[{"id":"https://openalex.org/I4210150356","display_name":"University of Washington Tacoma","ror":"https://ror.org/05n8t2628","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210150356"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shanu Sushmita","raw_affiliation_strings":["Center for Data Science, Institute of Technology, University of Washington, Tacoma"],"affiliations":[{"raw_affiliation_string":"Center for Data Science, Institute of Technology, University of Washington, Tacoma","institution_ids":["https://openalex.org/I4210150356"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056749857","display_name":"Martine De Cock","orcid":"https://orcid.org/0000-0001-7917-0771"},"institutions":[{"id":"https://openalex.org/I4210150356","display_name":"University of Washington Tacoma","ror":"https://ror.org/05n8t2628","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701","https://openalex.org/I4210150356"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martine De Cock","raw_affiliation_strings":["Center for Data Science, Institute of Technology, University of Washington, Tacoma and Ghent University"],"affiliations":[{"raw_affiliation_string":"Center for Data Science, Institute of Technology, University of Washington, Tacoma and Ghent University","institution_ids":["https://openalex.org/I4210150356"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5014759332"],"corresponding_institution_ids":["https://openalex.org/I4210150356"],"apc_list":null,"apc_paid":null,"fwci":2.1199,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.87307754,"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":"603","last_page":"610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9751999974250793,"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":0.960099995136261,"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/emergency-department","display_name":"Emergency department","score":0.8534721732139587},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7022671103477478},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6289492845535278},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5706841945648193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5574314594268799},{"id":"https://openalex.org/keywords/psychological-intervention","display_name":"Psychological intervention","score":0.5344067811965942},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5304871201515198},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49533072113990784},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.4772724509239197},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4709399938583374},{"id":"https://openalex.org/keywords/medical-emergency","display_name":"Medical emergency","score":0.43238168954849243},{"id":"https://openalex.org/keywords/emergency-medicine","display_name":"Emergency medicine","score":0.38441237807273865},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10260990262031555}],"concepts":[{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.8534721732139587},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7022671103477478},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6289492845535278},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5706841945648193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5574314594268799},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.5344067811965942},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5304871201515198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49533072113990784},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4772724509239197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4709399938583374},{"id":"https://openalex.org/C545542383","wikidata":"https://www.wikidata.org/wiki/Q2751242","display_name":"Medical emergency","level":1,"score":0.43238168954849243},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.38441237807273865},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10260990262031555},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2975167.2985845","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2975167.2985845","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1594031697","https://openalex.org/W1767329902","https://openalex.org/W1853053346","https://openalex.org/W1887152358","https://openalex.org/W1965434882","https://openalex.org/W1988790447","https://openalex.org/W1992803797","https://openalex.org/W1999718632","https://openalex.org/W2050674998","https://openalex.org/W2057780602","https://openalex.org/W2086140666","https://openalex.org/W2120489040","https://openalex.org/W2161123687","https://openalex.org/W2163614729","https://openalex.org/W2182173183","https://openalex.org/W2262426249","https://openalex.org/W2334028018","https://openalex.org/W2396530778","https://openalex.org/W2400993374","https://openalex.org/W3085162807","https://openalex.org/W3123368926","https://openalex.org/W6843735874"],"related_works":["https://openalex.org/W4384345078","https://openalex.org/W2791973335","https://openalex.org/W2384709362","https://openalex.org/W2079855793","https://openalex.org/W3039673966","https://openalex.org/W2097386058","https://openalex.org/W4367335967","https://openalex.org/W4387977367","https://openalex.org/W2141272333","https://openalex.org/W4385216705"],"abstract_inverted_index":{"A":[0],"large":[1],"percentage":[2,12],"of":[3,13,41,50,66,118,122,125,138,148,170],"emergency":[4,130],"department":[5],"(ED)":[6],"visits":[7,171],"originates":[8],"from":[9,72,95,98],"a":[10,160,190,194],"small":[11],"patients":[14],"who":[15],"keep":[16],"returning":[17],"to":[18,23,33,37,129,164,217,221],"the":[19,39,73,76,99,110,116,136],"ED.":[20],"Being":[21],"able":[22],"flag":[24],"these":[25],"frequent":[26,166,211],"users":[27,168,213,224],"in":[28,185],"advance":[29],"can":[30,61],"help":[31],"clinicians":[32],"take":[34],"appropriate":[35],"interventions":[36],"reduce":[38],"number":[40],"ED":[42,64,126,167,212,223],"visits,":[43,127],"thereby":[44],"reducing":[45],"cost":[46],"and":[47,75,87,102,104,132,157,200,209,228],"improving":[48],"quality":[49],"care.":[51],"In":[52],"this":[53,149],"paper":[54],"we":[55],"present":[56,74],"machine":[57],"learning":[58],"models":[59,91,176],"that":[60,197],"predict":[62,218],"future":[63],"utilization":[65],"individual":[67],"patients,":[68],"using":[69],"only":[70],"information":[71],"past.":[77],"We":[78,113],"train":[79],"decision":[80,84],"trees":[81,85],"(DT),":[82],"boosted":[83],"(AdaBoost)":[86],"logistic":[88],"regression":[89],"(LR)":[90],"on":[92,135],"discharge":[93],"records":[94],"California-licensed":[96],"hospitals":[97],"years":[100,111,184],"2009":[101],"2010,":[103],"evaluate":[105],"their":[106],"predictive":[107,140,162,195],"accuracy":[108,137],"for":[109],"2011-2013.":[112],"also":[114],"study":[115],"impact":[117],"including":[119],"different":[120],"groups":[121],"demographic,":[123],"frequency":[124],"distance":[128],"department,":[131],"clinical":[133],"features":[134],"our":[139,175,186],"models.":[141],"Overall":[142],"there":[143],"are":[144,214],"three":[145,153,182],"key":[146],"findings":[147],"study.":[150],"First,":[151],"all":[152,181],"techniques":[154],"(LR,":[155],"DT":[156],"AdaBoost)":[158],"have":[159],"strong":[161],"ability":[163],"discriminate":[165],"(number":[169],"\u2265":[172],"5).":[173],"Second,":[174],"show":[177],"consistent":[178,201],"outcomes":[179],"across":[180],"test":[183],"dataset,":[187],"which":[188],"is":[189,198,205],"desired":[191],"property":[192],"when":[193,219],"tool":[196],"stable":[199],"year":[202,204],"over":[203],"required.":[206],"Third,":[207],"least":[208],"most":[210],"comparatively":[215],"easier":[216],"compared":[220],"moderate":[222],"(with":[225],"higher":[226],"sensitivity":[227],"AUC":[229],"scores).":[230]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
