{"id":"https://openalex.org/W4205683422","doi":"https://doi.org/10.1109/bibm52615.2021.9669283","title":"A data-driven model for the generation of Virtual Cohorts","display_name":"A data-driven model for the generation of Virtual Cohorts","publication_year":2021,"publication_date":"2021-12-09","ids":{"openalex":"https://openalex.org/W4205683422","doi":"https://doi.org/10.1109/bibm52615.2021.9669283"},"language":"en","primary_location":{"id":"doi:10.1109/bibm52615.2021.9669283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669283","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":"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/A5053212610","display_name":"Enrico Mastrostefano","orcid":"https://orcid.org/0000-0002-0023-7943"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Enrico Mastrostefano","raw_affiliation_strings":["IAC-CNR, Rome, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAC-CNR, Rome, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009148451","display_name":"Paola Stolfi","orcid":"https://orcid.org/0000-0003-3688-5464"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paola Stolfi","raw_affiliation_strings":["IAC-CNR, Rome, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAC-CNR, Rome, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010454141","display_name":"Filippo Castiglione","orcid":"https://orcid.org/0000-0002-1442-3552"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Filippo Castiglione","raw_affiliation_strings":["IAC-CNR, Rome, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IAC-CNR, Rome, Italy","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3291","last_page":"3298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10261","display_name":"Genetic Associations and Epidemiology","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11510","display_name":"Reproductive System and Pregnancy","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10833","display_name":"Vaccine Coverage and Hesitancy","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/3306","display_name":"Health"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.9084831476211548},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6054427623748779},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5449972152709961},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4732416570186615},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.44784724712371826},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4149315059185028},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3514193296432495},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3375943601131439},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33541250228881836},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.32016563415527344},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.24151822924613953},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15103182196617126}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.9084831476211548},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6054427623748779},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5449972152709961},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4732416570186615},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.44784724712371826},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4149315059185028},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3514193296432495},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3375943601131439},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33541250228881836},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.32016563415527344},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.24151822924613953},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15103182196617126},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm52615.2021.9669283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm52615.2021.9669283","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W621293842","https://openalex.org/W1562323213","https://openalex.org/W1591219715","https://openalex.org/W1896453849","https://openalex.org/W1922498403","https://openalex.org/W1971463294","https://openalex.org/W1977523316","https://openalex.org/W1997153955","https://openalex.org/W1998653899","https://openalex.org/W2001747862","https://openalex.org/W2014266841","https://openalex.org/W2016658076","https://openalex.org/W2038483136","https://openalex.org/W2039559136","https://openalex.org/W2057622893","https://openalex.org/W2059833361","https://openalex.org/W2063996354","https://openalex.org/W2104120663","https://openalex.org/W2133840634","https://openalex.org/W2138955470","https://openalex.org/W2143726924","https://openalex.org/W2171362509","https://openalex.org/W2304315762","https://openalex.org/W2345592718","https://openalex.org/W2487770199","https://openalex.org/W2797791374","https://openalex.org/W2802525560","https://openalex.org/W2805754304","https://openalex.org/W2806810105","https://openalex.org/W2922666040","https://openalex.org/W2953377360","https://openalex.org/W2974822460","https://openalex.org/W2999047493","https://openalex.org/W3001465925","https://openalex.org/W3004442407","https://openalex.org/W3027941160","https://openalex.org/W3110784443","https://openalex.org/W3113072416","https://openalex.org/W3121700981","https://openalex.org/W3146049360","https://openalex.org/W3161306590","https://openalex.org/W3164306789","https://openalex.org/W3196491939","https://openalex.org/W7025560132"],"related_works":["https://openalex.org/W2985746494","https://openalex.org/W4206042385","https://openalex.org/W2511384863","https://openalex.org/W2096089271","https://openalex.org/W2923628599","https://openalex.org/W2051519658","https://openalex.org/W2002304499","https://openalex.org/W2994787386","https://openalex.org/W2014100433","https://openalex.org/W3088459959"],"abstract_inverted_index":{"In":[0],"silico":[1],"trials":[2],"are":[3],"emerging":[4],"as":[5],"a":[6,26,30,48,56,60,82,134,146],"valuable":[7],"tool":[8],"for":[9],"improving":[10],"both":[11],"study":[12,44],"design":[13],"and":[14,55,98,122],"outcomes.":[15],"A":[16,67],"key":[17],"component":[18],"of":[19,25,32,62,111,124,128,140],"this":[20],"process":[21],"is":[22,81,90,99,107],"the":[23,42,73,112,120,125,138,150],"definition":[24],"virtual":[27,33,65],"cohort,":[28],"i.e.,":[29],"set":[31],"patients":[34],"with":[35],"plausible":[36,63],"physiological":[37],"characteristics":[38],"(covariates).":[39],"Building":[40],"on":[41,137,149],"NHANES":[43],"(2017-2020),":[45],"we":[46],"developed":[47],"statistical":[49,69],"model":[50,76],"to":[51,58,77,87,92],"infer":[52],"immunological":[53,64,152],"parameters":[54],"technique":[57],"generate":[59],"population":[61],"patients.":[66],"thorough":[68],"analysis":[70,117],"showed":[71],"that":[72],"most":[74],"appropriate":[75],"represent":[78],"our":[79],"data":[80],"conditional":[83],"multivariate":[84],"model.":[85],"Compared":[86],"others,":[88],"it":[89],"able":[91],"reproduce":[93],"asymmetric":[94],"distributions":[95],"more":[96,101],"accurately":[97],"therefore":[100],"suitable":[102],"in":[103],"cases":[104],"where":[105],"there":[106],"no":[108],"prior":[109],"knowledge":[110],"relationships":[113],"between":[114],"covariates.":[115,153],"Our":[116],"also":[118],"demonstrates":[119],"inter-variability":[121],"inter-dependence":[123],"different":[126],"covariates":[127],"interest.":[129],"For":[130],"example,":[131],"age":[132],"has":[133,145],"negative":[135],"impact":[136],"number":[139],"lymphocytes":[141],"and,":[142],"surprisingly,":[143],"ethnicity":[144],"minor":[147],"influence":[148],"other":[151]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
