{"id":"https://openalex.org/W3012412017","doi":"https://doi.org/10.1117/12.2550545","title":"Simulation of multi-reader multi-case study data with realistic ROC performance characteristics","display_name":"Simulation of multi-reader multi-case study data with realistic ROC performance characteristics","publication_year":2020,"publication_date":"2020-03-16","ids":{"openalex":"https://openalex.org/W3012412017","doi":"https://doi.org/10.1117/12.2550545","mag":"3012412017"},"language":"en","primary_location":{"id":"doi:10.1117/12.2550545","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550545","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment","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/A5101953386","display_name":"Xiaochen Zhu","orcid":"https://orcid.org/0000-0002-9499-6464"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaochen Zhu","raw_affiliation_strings":["George Mason Univ. (United States)"],"affiliations":[{"raw_affiliation_string":"George Mason Univ. (United States)","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020066887","display_name":"Wei-Jie Chen","orcid":"https://orcid.org/0000-0001-5186-0279"},"institutions":[{"id":"https://openalex.org/I1320320070","display_name":"United States Food and Drug Administration","ror":"https://ror.org/034xvzb47","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1320320070"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weijie Chen","raw_affiliation_strings":["U.S. FDA/CDRH/OSEL (United States)"],"affiliations":[{"raw_affiliation_string":"U.S. FDA/CDRH/OSEL (United States)","institution_ids":["https://openalex.org/I1320320070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101953386"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.2939,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.68913393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"20","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14400","display_name":"Medical Coding and Health Information","score":0.9657999873161316,"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"}},"topics":[{"id":"https://openalex.org/T14400","display_name":"Medical Coding and Health Information","score":0.9657999873161316,"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":0.9599000215530396,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9460999965667725,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6089168190956116},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.5630030632019043},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5578135251998901},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.540062665939331},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.5109180808067322},{"id":"https://openalex.org/keywords/random-effects-model","display_name":"Random effects model","score":0.4930672347545624},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.46033769845962524},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4182104766368866},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31294509768486023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2647697329521179}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6089168190956116},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5630030632019043},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5578135251998901},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.540062665939331},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5109180808067322},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.4930672347545624},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.46033769845962524},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4182104766368866},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31294509768486023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2647697329521179},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C95190672","wikidata":"https://www.wikidata.org/wiki/Q815382","display_name":"Meta-analysis","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2550545","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2550545","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6600000262260437,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2118717649","https://openalex.org/W2413243053","https://openalex.org/W410723623","https://openalex.org/W2015341305","https://openalex.org/W2035068594","https://openalex.org/W4225593417","https://openalex.org/W2573498121","https://openalex.org/W3022298670","https://openalex.org/W3160494304","https://openalex.org/W2189229849"],"abstract_inverted_index":{"Multi-reader":[0],"multi-case":[1],"(MRMC)":[2],"studies":[3,235],"are":[4],"widely":[5],"used":[6],"in":[7,44,79],"assessing":[8,80],"medical":[9],"imaging":[10],"and":[11,29,62,101,114,159,174,202,211],"computer-aided":[12],"diagnosis":[13],"devices":[14],"to":[15,22,135,139,169,192,238],"demonstrate":[16],"the":[17,24,30,121,145,171,182,194,199,208,231],"generalizability":[18],"of":[19,26,32,36,83,88,124,131,157,181],"diagnostic":[20],"performance":[21,151,228],"both":[23],"population":[25,31],"patient":[27],"cases":[28],"physician":[33],"readers.":[34],"Simulation":[35,234],"MRMC":[37,46],"study":[38,54,222],"data":[39,47,147,225],"plays":[40],"an":[41],"important":[42],"role":[43],"validating":[45],"analysis":[48],"methods":[49],"or":[50],"sizing":[51],"a":[52,67,73,86,102,111,125,137,189],"pivotal":[53],"based":[55],"on":[56],"pilot":[57],"data.":[58,233],"The":[59,105,129],"popular":[60],"Roe":[61],"Metz":[63],"simulation":[64,183,195],"model":[65,70,184,196],"is":[66,108,118,134],"linear":[68],"mixed-effect":[69],"that":[71,144],"models":[72],"human":[74],"reader's":[75],"latent":[76],"decision":[77],"variable":[78],"patient's":[81],"likelihood":[82],"disease":[84],"as":[85,179],"sum":[87],"fixed":[89,106],"modality":[90],"effect,":[91,94,97],"random":[92,95,98,103,116],"reader":[93,221],"case":[96],"interaction":[99],"effects,":[100],"error.":[104],"effect":[107,117],"represented":[109,119],"by":[110,120],"mean":[112,172,200,209],"parameter":[113,123,241],"each":[115],"variance":[122,155,177,205,214],"zero-mean":[126],"Gaussian":[127],"distribution.":[128],"purpose":[130],"this":[132,163],"paper":[133],"develop":[136],"method":[138],"set":[140],"these":[141],"parameters":[142,197],"such":[143],"simulated":[146,224],"have":[148,226],"realistic":[149],"ROC":[150],"characteristics":[152,229],"(mean":[153],"AUC,":[154,158],"components":[156,178,215],"inter-reader/inter-modality":[160],"correlations).":[161],"To":[162],"end,":[164],"we":[165],"derived":[166],"quasi-closed-form":[167],"expressions":[168],"express":[170],"AUC":[173,201,210],"its":[175,203,212],"U-statistic":[176,204,213],"functions":[180],"parameters.":[185],"We":[186],"then":[187],"developed":[188],"numerical":[190],"algorithm":[191],"solve":[193],"from":[198,219],"components.":[206],"Since":[207],"can":[216],"be":[217],"estimated":[218],"real-world":[220,232],"data,":[223],"similar":[227],"with":[230],"were":[236],"conducted":[237],"verify":[239],"our":[240],"transformation":[242],"algorithm.":[243]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
