{"id":"https://openalex.org/W4205934373","doi":"https://doi.org/10.1109/bibm52615.2021.9669288","title":"Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework","display_name":"Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4205934373","doi":"https://doi.org/10.1109/bibm52615.2021.9669288"},"language":"en","primary_location":{"id":"doi:10.1109/bibm52615.2021.9669288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669288","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/20.500.11824/1446","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041627450","display_name":"Rub\u00e9n Arma\u00f1anzas","orcid":"https://orcid.org/0000-0003-4049-0000"},"institutions":[{"id":"https://openalex.org/I2802176441","display_name":"Basque Center for Applied Mathematics","ror":"https://ror.org/03b21sh32","country_code":"ES","type":"education","lineage":["https://openalex.org/I2802176441"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Ruben Armananzas","raw_affiliation_strings":["Data Science Area Basque Center for Applied Mathematics, Bilbao, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Area Basque Center for Applied Mathematics, Bilbao, Spain","institution_ids":["https://openalex.org/I2802176441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100747383","display_name":"Adri\u00e1n D\u00edaz","orcid":"https://orcid.org/0000-0002-2876-6177"},"institutions":[{"id":"https://openalex.org/I2802176441","display_name":"Basque Center for Applied Mathematics","ror":"https://ror.org/03b21sh32","country_code":"ES","type":"education","lineage":["https://openalex.org/I2802176441"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Adrian Diaz","raw_affiliation_strings":["Data Science Area Basque Center for Applied Mathematics, Bilbao, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Area Basque Center for Applied Mathematics, Bilbao, Spain","institution_ids":["https://openalex.org/I2802176441"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059506785","display_name":"Mario Mart\u00ednez-Garc\u00eda","orcid":"https://orcid.org/0000-0002-6849-6239"},"institutions":[{"id":"https://openalex.org/I2802176441","display_name":"Basque Center for Applied Mathematics","ror":"https://ror.org/03b21sh32","country_code":"ES","type":"education","lineage":["https://openalex.org/I2802176441"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mario Martinez-Garcia","raw_affiliation_strings":["Data Science Area Basque Center for Applied Mathematics, Bilbao, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Area Basque Center for Applied Mathematics, Bilbao, Spain","institution_ids":["https://openalex.org/I2802176441"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063455689","display_name":"Santiago Mazuelas","orcid":"https://orcid.org/0000-0002-6608-8581"},"institutions":[{"id":"https://openalex.org/I110594554","display_name":"Ikerbasque","ror":"https://ror.org/01cc3fy72","country_code":"ES","type":"other","lineage":["https://openalex.org/I110594554"]},{"id":"https://openalex.org/I2802176441","display_name":"Basque Center for Applied Mathematics","ror":"https://ror.org/03b21sh32","country_code":"ES","type":"education","lineage":["https://openalex.org/I2802176441"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Santiago Mazuelas","raw_affiliation_strings":["Data Science Area Basque Center for Applied Mathematics IKERBASQUE Basque Foundation for Science, Bilbao, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Data Science Area Basque Center for Applied Mathematics IKERBASQUE Basque Foundation for Science, Bilbao, Spain","institution_ids":["https://openalex.org/I110594554","https://openalex.org/I2802176441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6052,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60862983,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"17","issue":null,"first_page":"2179","last_page":"2186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9990000128746033,"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":0.9990000128746033,"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/T10041","display_name":"COVID-19 Clinical Research Studies","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2725","display_name":"Infectious Diseases"},"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":0.9879000186920166,"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/overfitting","display_name":"Overfitting","score":0.6575837135314941},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5206077098846436},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5205903053283691},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5133058428764343},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.5048118829727173},{"id":"https://openalex.org/keywords/cutoff","display_name":"Cutoff","score":0.5047045946121216},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.466942697763443},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4604763090610504},{"id":"https://openalex.org/keywords/mortality-rate","display_name":"Mortality rate","score":0.42894411087036133},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4049430787563324},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.380941778421402},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3741031289100647},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.35610154271125793},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3487640619277954},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34730857610702515},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2572978734970093},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.16161513328552246},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14606952667236328},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1318606436252594}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6575837135314941},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5206077098846436},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5205903053283691},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5133058428764343},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5048118829727173},{"id":"https://openalex.org/C2778217198","wikidata":"https://www.wikidata.org/wiki/Q556977","display_name":"Cutoff","level":2,"score":0.5047045946121216},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.466942697763443},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4604763090610504},{"id":"https://openalex.org/C179755657","wikidata":"https://www.wikidata.org/wiki/Q58702","display_name":"Mortality rate","level":2,"score":0.42894411087036133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4049430787563324},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.380941778421402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3741031289100647},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.35610154271125793},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3487640619277954},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34730857610702515},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2572978734970093},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.16161513328552246},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14606952667236328},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1318606436252594},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bibm52615.2021.9669288","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669288","pdf_url":null,"source":{"id":"https://openalex.org/S4363607735","display_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},{"id":"pmh:oai:bird.bcamath.org:20.500.11824/1446","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11824/1446","pdf_url":null,"source":{"id":"https://openalex.org/S4306401608","display_name":"BIRD (Basque Center for Applied Mathematics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2802176441","host_organization_name":"Basque Center for Applied Mathematics","host_organization_lineage":["https://openalex.org/I2802176441"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:bird.bcamath.org:20.500.11824/1446","is_oa":true,"landing_page_url":"http://hdl.handle.net/20.500.11824/1446","pdf_url":null,"source":{"id":"https://openalex.org/S4306401608","display_name":"BIRD (Basque Center for Applied Mathematics)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2802176441","host_organization_name":"Basque Center for Applied Mathematics","host_organization_lineage":["https://openalex.org/I2802176441"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8799999952316284,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W167016754","https://openalex.org/W767037412","https://openalex.org/W2096863518","https://openalex.org/W2115358726","https://openalex.org/W2143426320","https://openalex.org/W2197051856","https://openalex.org/W2962862931","https://openalex.org/W3011605790","https://openalex.org/W3014524604","https://openalex.org/W3025394897","https://openalex.org/W3036031606","https://openalex.org/W3046629770","https://openalex.org/W3047507912","https://openalex.org/W3048557429","https://openalex.org/W3094264462","https://openalex.org/W3097787292","https://openalex.org/W3099462644","https://openalex.org/W3099646818","https://openalex.org/W3104241090","https://openalex.org/W3120017613","https://openalex.org/W3121555817","https://openalex.org/W3131170816","https://openalex.org/W6606837198","https://openalex.org/W6737947904","https://openalex.org/W6775753438","https://openalex.org/W6779320463","https://openalex.org/W6785134789"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4221142204","https://openalex.org/W757031997"],"abstract_inverted_index":{"The":[0,81,120,173],"overall":[1],"global":[2],"death":[3],"rate":[4],"for":[5,48,78,132,187,230],"COVID-19":[6,97,268],"patients":[7,93,208],"has":[8],"escalated":[9],"to":[10,45,63,70,130,149,246,250],"2.13%":[11],"after":[12],"more":[13],"than":[14],"a":[15,32,42,50,59,65,75,89,106,126,164,168,200,220,251],"year":[16],"of":[17,74,83,91,125,181,197,206,257,263],"worldwide":[18],"spread.":[19],"Despite":[20],"strong":[21,252],"research":[22],"on":[23],"the":[24,27,72,84,95,133,155,184,188,191,212,231,236,255,261],"infection":[25],"pathogenesis,":[26],"molecular":[28],"mechanisms":[29],"involved":[30],"in":[31,99,267],"fatal":[33,76],"course":[34],"are":[35],"still":[36],"poorly":[37],"understood.":[38],"Machine":[39],"learning":[40,102],"constitutes":[41],"perfect":[43,216],"tool":[44],"develop":[46],"algorithms":[47],"predicting":[49],"patient\u2019s":[51],"hospitalization":[52],"outcome":[53,77,166],"at":[54],"triage.":[55],"This":[56],"paper":[57],"presents":[58],"probabilistic":[60],"model,":[61],"referred":[62],"as":[64,271],"mortality":[66,134,160,170],"risk":[67,73,171],"indicator,":[68],"able":[69],"assess":[71],"new":[79],"patients.":[80],"derivation":[82],"model":[85,192],"was":[86,103],"done":[87],"over":[88],"database":[90],"2,547":[92],"from":[94,211],"first":[96],"wave":[98],"Spain.":[100],"Model":[101],"tackled":[104],"through":[105],"five":[107],"multistart":[108],"configuration":[109],"that":[110],"guaranteed":[111],"good":[112],"generalization":[113],"power":[114],"and":[115,137,142,167,275],"low":[116],"variance":[117],"error":[118],"estimators.":[119],"training":[121],"algorithm":[122],"made":[123],"use":[124],"class":[127,135],"weighting":[128],"correction":[129],"account":[131],"imbalance":[136],"two":[138,242],"regularization":[139],"learners,":[140],"logistic":[141],"lasso":[143],"regressors.":[144],"Outcome":[145],"probabilities":[146],"were":[147],"adjusted":[148],"obtain":[150],"cost-sensitive":[151],"predictions":[152],"by":[153,225],"minimizing":[154],"type":[156],"II":[157],"error.":[158],"Our":[159],"indicator":[161,232],"returns":[162],"both":[163],"binary":[165,189],"three-stage":[169],"level.":[172],"estimated":[174],"AUC":[175],"across":[176],"multistarts":[177],"reaches":[178],"an":[179,194],"average":[180,195],"0.907.":[182],"At":[183],"optimal":[185],"cutoff":[186],"outcome,":[190],"attains":[193],"sensitivity":[196,217],"0.898,":[198],"with":[199,219],"0.745":[201],"specificity.":[202],"An":[203],"independent":[204],"set":[205,256],"121":[207],"later":[209],"released":[210],"same":[213],"consortium":[214],"attained":[215],"(1),":[218],"0.759":[221],"specificity":[222],"when":[223,235],"predicted":[224],"our":[226],"model.":[227],"Best":[228],"performance":[229],"is":[233,240],"achieved":[234],"prediction\u2019s":[237],"time":[238],"horizon":[239],"within":[241],"weeks":[243],"since":[244],"admission":[245],"hospital.":[247],"In":[248],"addition":[249],"predictive":[253],"performance,":[254],"selected":[258],"features":[259],"highlights":[260],"relevance":[262],"several":[264],"underrated":[265],"molecules":[266],"research,":[269],"such":[270],"blood":[272],"eosinophils,":[273],"bilirubin,":[274],"urea":[276],"levels.":[277]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-02T09:51:11.867554","created_date":"2025-10-10T00:00:00"}
