{"id":"https://openalex.org/W3129004367","doi":"https://doi.org/10.1145/3437378.3437382","title":"On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models","display_name":"On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models","publication_year":2021,"publication_date":"2021-02-01","ids":{"openalex":"https://openalex.org/W3129004367","doi":"https://doi.org/10.1145/3437378.3437382","mag":"3129004367"},"language":"en","primary_location":{"id":"doi:10.1145/3437378.3437382","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437378.3437382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Australasian Computer Science Week Multiconference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11343/265778","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032150204","display_name":"Yuxuan Yang","orcid":"https://orcid.org/0009-0004-0906-7292"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Yuxuan Y Yang","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016445829","display_name":"Hadi Akbarzadeh Khorshidi","orcid":"https://orcid.org/0000-0002-2653-4102"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hadi Akbarzadeh HA Khorshidi","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002768704","display_name":"Uwe Aickelin","orcid":"https://orcid.org/0000-0002-2679-2275"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Uwe U Aickelin","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070970187","display_name":"Aditi Nevgi","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Aditi A Nevgi","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073232912","display_name":"Elif Ekinci","orcid":null},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Elif E Ekinci","raw_affiliation_strings":["The University of Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"The University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032150204"],"corresponding_institution_ids":["https://openalex.org/I165779595"],"apc_list":null,"apc_paid":null,"fwci":0.5589,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.71776207,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T14400","display_name":"Medical Coding and Health Information","score":0.9972000122070312,"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":0.972599983215332,"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/sort","display_name":"sort","score":0.6840342283248901},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.683964729309082},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6485106945037842},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5342774391174316},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49800968170166016},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4401223659515381},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.1526457667350769}],"concepts":[{"id":"https://openalex.org/C88548561","wikidata":"https://www.wikidata.org/wiki/Q347599","display_name":"sort","level":2,"score":0.6840342283248901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.683964729309082},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6485106945037842},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5342774391174316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49800968170166016},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4401223659515381},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.1526457667350769}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3437378.3437382","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3437378.3437382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 Australasian Computer Science Week Multiconference","raw_type":"proceedings-article"},{"id":"pmh:oai:jupiter.its.unimelb.edu.au:11343/265778","is_oa":true,"landing_page_url":"http://hdl.handle.net/11343/265778","pdf_url":"http://hdl.handle.net/11343/265778","source":{"id":"https://openalex.org/S4377196259","display_name":"Minerva Access (University of Melbourne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165779595","host_organization_name":"The University of Melbourne","host_organization_lineage":["https://openalex.org/I165779595"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2021 Australasian Computer Science Week Multiconference","raw_type":"Conference Paper"}],"best_oa_location":{"id":"pmh:oai:jupiter.its.unimelb.edu.au:11343/265778","is_oa":true,"landing_page_url":"http://hdl.handle.net/11343/265778","pdf_url":"http://hdl.handle.net/11343/265778","source":{"id":"https://openalex.org/S4377196259","display_name":"Minerva Access (University of Melbourne)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165779595","host_organization_name":"The University of Melbourne","host_organization_lineage":["https://openalex.org/I165779595"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2021 Australasian Computer Science Week Multiconference","raw_type":"Conference Paper"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3129004367.pdf"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W639218804","https://openalex.org/W1588282782","https://openalex.org/W1766594731","https://openalex.org/W1966716734","https://openalex.org/W1970879550","https://openalex.org/W1976526581","https://openalex.org/W1996523702","https://openalex.org/W2026316367","https://openalex.org/W2051947720","https://openalex.org/W2064076120","https://openalex.org/W2073141423","https://openalex.org/W2087240369","https://openalex.org/W2132791018","https://openalex.org/W2148143831","https://openalex.org/W2562319768","https://openalex.org/W2614183994","https://openalex.org/W2725294665","https://openalex.org/W2775947831","https://openalex.org/W2907754184","https://openalex.org/W2948219206","https://openalex.org/W3090040197","https://openalex.org/W3090910946","https://openalex.org/W4245807786","https://openalex.org/W4285719527","https://openalex.org/W4399647908","https://openalex.org/W6737563499"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4313488044","https://openalex.org/W3209574120","https://openalex.org/W4312192474","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Surgical":[0,38],"risk":[1,18,28,52],"increases":[2],"significantly":[3],"when":[4],"patients":[5,34],"present":[6],"with":[7,21,156],"comorbid":[8],"conditions.":[9],"This":[10],"has":[11],"resulted":[12],"in":[13,35,63,106,119],"the":[14,22,46,54,64,71,80,84,90,103,128,134,143,146,157,169,179,182,189,201,204],"creation":[15],"of":[16,24,45,92,97,127,137,145,159,181,203],"numerous":[17],"stratification":[19],"tools":[20,47],"objective":[23],"formulating":[25],"associated":[26],"surgical":[27],"to":[29,49,101,153,162],"assist":[30],"both":[31],"surgeons":[32],"and":[33,164],"decision-making.":[36],"The":[37],"Outcome":[39],"Risk":[40],"Tool":[41],"(SORT)":[42],"is":[43,115],"one":[44],"developed":[48],"predict":[50],"mortality":[51],"throughout":[53],"entire":[55],"perioperative":[56],"period":[57],"for":[58],"major":[59],"elective":[60],"in-patient":[61],"surgeries":[62],"UK.":[65],"In":[66],"this":[67],"study,":[68],"we":[69,176],"enhance":[70,102],"original":[72,190],"SORT":[73,206],"prediction":[74],"model":[75],"(UK":[76],"SORT)":[77],"by":[78,208],"addressing":[79],"class":[81,130],"imbalance":[82],"within":[83],"dataset.":[85],"Our":[86,196],"proposed":[87],"method":[88,199],"investigates":[89],"application":[91],"diversity-based":[93,186,197],"selection":[94],"on":[95],"top":[96],"common":[98],"re-sampling":[99,198],"techniques":[100],"classifier's":[104],"capability":[105],"detecting":[107],"minority":[108],"(\u2018mortality\u2019)":[109],"events.":[110],"Diversity":[111],"amongst":[112],"training":[113],"datasets":[114],"an":[116,124],"essential":[117],"factor":[118],"ensuring":[120],"re-sampled":[121],"data":[122],"keeps":[123],"accurate":[125],"depiction":[126],"minority/majority":[129],"region,":[131],"thereby":[132],"solving":[133],"generalization":[135],"problem":[136],"mainstream":[138],"sampling":[139],"approaches.":[140],"We":[141],"incorporate":[142],"use":[144],"Solow-Polasky":[147],"measure":[148],"as":[149],"a":[150],"drop-in":[151],"functionality":[152],"evaluate":[154],"diversity,":[155],"addition":[158],"greedy":[160],"algorithms":[161],"identify":[163],"discard":[165],"subsets":[166],"that":[167,178],"share":[168],"most":[170],"similarity.":[171],"Additionally,":[172],"through":[173],"empirical":[174],"experiments,":[175],"prove":[177],"performance":[180,202],"classifier":[183,191],"trained":[184],"over":[185,192],"dataset":[187],"outperforms":[188],"ten":[193],"external":[194],"datasets.":[195],"elevates":[200],"UK":[205],"algorithm":[207],"1.4%.":[209]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
