{"id":"https://openalex.org/W4406458099","doi":"https://doi.org/10.1109/bigdata62323.2024.10825572","title":"Evaluating and Reducing Subgroup Disparity in AI Models Predicting Pediatric COVID-19 Test Outcomes","display_name":"Evaluating and Reducing Subgroup Disparity in AI Models Predicting Pediatric COVID-19 Test Outcomes","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458099","doi":"https://doi.org/10.1109/bigdata62323.2024.10825572"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5021762893","display_name":"Alexander Libin","orcid":"https://orcid.org/0000-0001-7847-3463"},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]},{"id":"https://openalex.org/I4210120018","display_name":"Georgetown-Howard Universities Center for Clinical and Translational Science","ror":"https://ror.org/02fz54z33","country_code":"US","type":"education","lineage":["https://openalex.org/I4210120018"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexander Libin","raw_affiliation_strings":["AIM AHEAD Consortium, Georgetown-Howard Universities Center for Clinical and Translational Science, Medstar Research Health Institute, Georgetown University,Washington,D.C.,USA"],"affiliations":[{"raw_affiliation_string":"AIM AHEAD Consortium, Georgetown-Howard Universities Center for Clinical and Translational Science, Medstar Research Health Institute, Georgetown University,Washington,D.C.,USA","institution_ids":["https://openalex.org/I4210120018","https://openalex.org/I184565670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092693619","display_name":"Jonah T. Treitler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonah Treitler","raw_affiliation_strings":["Thomas Jefferson High School for Science and Technology,Arlington,VA,USA"],"affiliations":[{"raw_affiliation_string":"Thomas Jefferson High School for Science and Technology,Arlington,VA,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107418890","display_name":"Tadas Vasaitis","orcid":null},"institutions":[{"id":"https://openalex.org/I22407884","display_name":"University of Maryland Eastern Shore","ror":"https://ror.org/006cymg18","country_code":"US","type":"education","lineage":["https://openalex.org/I22407884"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tadas Vasaitis","raw_affiliation_strings":["University of Maryland Eastern Shore,School of Pharmacy and Health Professions,Princess Anne,MD,USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland Eastern Shore,School of Pharmacy and Health Professions,Princess Anne,MD,USA","institution_ids":["https://openalex.org/I22407884"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084025490","display_name":"Yijun Shao","orcid":"https://orcid.org/0000-0001-6419-7963"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yijun Shao","raw_affiliation_strings":["Biomedical Informatics Center, George Washington University,Washington,D.C.,USA"],"affiliations":[{"raw_affiliation_string":"Biomedical Informatics Center, George Washington University,Washington,D.C.,USA","institution_ids":["https://openalex.org/I193531525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5021762893"],"corresponding_institution_ids":["https://openalex.org/I184565670","https://openalex.org/I4210120018"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.43350103,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5033","last_page":"5042"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9922999739646912,"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/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.7214757204055786},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.5607128739356995},{"id":"https://openalex.org/keywords/2019-20-coronavirus-outbreak","display_name":"2019-20 coronavirus outbreak","score":0.5045055150985718},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4917435944080353},{"id":"https://openalex.org/keywords/severe-acute-respiratory-syndrome-coronavirus-2","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","score":0.4806757867336273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40287289023399353},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3313574194908142},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33131009340286255},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.28871673345565796},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16990187764167786},{"id":"https://openalex.org/keywords/virology","display_name":"Virology","score":0.12974673509597778},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.12577176094055176}],"concepts":[{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.7214757204055786},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.5607128739356995},{"id":"https://openalex.org/C3006700255","wikidata":"https://www.wikidata.org/wiki/Q81068910","display_name":"2019-20 coronavirus outbreak","level":3,"score":0.5045055150985718},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4917435944080353},{"id":"https://openalex.org/C3007834351","wikidata":"https://www.wikidata.org/wiki/Q82069695","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","level":5,"score":0.4806757867336273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40287289023399353},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3313574194908142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33131009340286255},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.28871673345565796},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16990187764167786},{"id":"https://openalex.org/C159047783","wikidata":"https://www.wikidata.org/wiki/Q7215","display_name":"Virology","level":1,"score":0.12974673509597778},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.12577176094055176},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C116675565","wikidata":"https://www.wikidata.org/wiki/Q3241045","display_name":"Outbreak","level":2,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2007233978","https://openalex.org/W2517582793","https://openalex.org/W2557738935","https://openalex.org/W3006389498","https://openalex.org/W3092398080","https://openalex.org/W3138202855","https://openalex.org/W3174786846","https://openalex.org/W3175311671","https://openalex.org/W3198960517","https://openalex.org/W4214681072","https://openalex.org/W4220760463","https://openalex.org/W4225690829","https://openalex.org/W4285493367","https://openalex.org/W4302027500","https://openalex.org/W4309006743","https://openalex.org/W4318393059","https://openalex.org/W4318485191","https://openalex.org/W4319662928","https://openalex.org/W4320855790","https://openalex.org/W4324128657","https://openalex.org/W4381716616","https://openalex.org/W4381893868","https://openalex.org/W4383818686","https://openalex.org/W4384338970","https://openalex.org/W4385462450","https://openalex.org/W4386002419","https://openalex.org/W4386083800","https://openalex.org/W4386397144","https://openalex.org/W4387232979","https://openalex.org/W4387653139","https://openalex.org/W4387753828","https://openalex.org/W4388229648","https://openalex.org/W4388750554"],"related_works":["https://openalex.org/W3036314732","https://openalex.org/W3009669391","https://openalex.org/W3176864053","https://openalex.org/W4206669628","https://openalex.org/W3171943759","https://openalex.org/W4292098121","https://openalex.org/W3154141118","https://openalex.org/W4388896133","https://openalex.org/W3031607536","https://openalex.org/W3198183218"],"abstract_inverted_index":{"Artificial":[0],"Intelligence":[1],"(AI)":[2],"fairness":[3,29],"in":[4,111,158,170],"healthcare":[5],"settings":[6],"has":[7],"attracted":[8],"significant":[9,146],"attention":[10],"due":[11],"to":[12,15,128],"the":[13,23,49,75,79,87,97,99,112,131,153,173],"concerns":[14],"propagate":[16],"existing":[17],"health":[18],"disparities.":[19,161],"Despite":[20],"ongoing":[21],"research,":[22],"frequency":[24],"and":[25,191],"extent":[26],"of":[27,86,123,141,155,164,172],"subgroup":[28,61,106,168],"have":[30],"not":[31,134],"been":[32],"sufficiently":[33],"studied.":[34],"In":[35],"this":[36],"study,":[37],"we":[38,64,151],"extracted":[39],"a":[40,120],"nationally":[41],"representative":[42],"pediatric":[43],"dataset":[44],"(ages":[45],"0-17,":[46],"n=9,935)":[47],"from":[48,126],"US":[50],"National":[51],"Health":[52],"Interview":[53],"Survey":[54],"(NHIS)":[55],"concerning":[56],"COVID-19":[57],"test":[58],"outcomes.":[59],"For":[60],"disparity":[62,169],"assessment,":[63],"trained":[65],"50":[66],"models":[67,180],"using":[68,92],"five":[69],"machine":[70],"learning":[71],"algorithms.":[72],"We":[73],"assessed":[74],"models\u2019":[76],"area":[77],"under":[78],"curve":[80],"(AUC)":[81],"on":[82,98,185],"12":[83,142],"small":[84],"(<15%":[85],"total":[88],"n)":[89],"subgroups":[90,143],"defined":[91],"social":[93],"economic":[94],"factors":[95],"versus":[96],"overall":[100],"population.":[101],"Our":[102],"results":[103],"show":[104],"that":[105],"disparities":[107,132,147,178],"were":[108,116,133],"prevalent":[109],"(50.7%)":[110],"models.":[113,149,174],"Subgroup":[114],"AUCs":[115],"generally":[117],"lower,":[118],"with":[119,138,181],"mean":[121,176],"difference":[122],"0.01,":[124],"ranging":[125],"-0.29":[127],"+0.41.":[129],"Notably,":[130],"always":[135],"statistically":[136,145],"significant,":[137],"four":[139],"out":[140],"having":[144],"across":[148],"Additionally,":[150],"explored":[152],"efficacy":[154],"synthetic":[156,165,182],"data":[157,166,183],"mitigating":[159],"identified":[160],"The":[162,175],"introduction":[163],"enhanced":[167],"57.7%":[171],"AUC":[177],"for":[179],"decreased":[184],"average":[186],"by":[187],"0.03":[188],"via":[189,193],"resampling":[190],"0.04":[192],"generative":[194],"adversarial":[195],"network":[196],"methods.":[197]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
