{"id":"https://openalex.org/W4297900505","doi":"https://doi.org/10.3389/frai.2022.918813","title":"Generating high-fidelity privacy-conscious synthetic patient data for causal effect estimation with multiple treatments","display_name":"Generating high-fidelity privacy-conscious synthetic patient data for causal effect estimation with multiple treatments","publication_year":2022,"publication_date":"2022-09-14","ids":{"openalex":"https://openalex.org/W4297900505","doi":"https://doi.org/10.3389/frai.2022.918813","pmid":"https://pubmed.ncbi.nlm.nih.gov/36187323","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/9515575"},"language":"en","primary_location":{"id":"doi:10.3389/frai.2022.918813","doi":"https://doi.org/10.3389/frai.2022.918813","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2022.918813","pdf_url":"https://www.frontiersin.org/articles/10.3389/frai.2022.918813/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/articles/10.3389/frai.2022.918813/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067375335","display_name":"Jingpu Shi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210115830","display_name":"Anthem (United States)","ror":"https://ror.org/01geb0342","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingpu Shi","raw_affiliation_strings":["Anthem AI, Palo Alto, CA, United States","Anthem AI, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anthem AI, Palo Alto, CA, United States","institution_ids":[]},{"raw_affiliation_string":"Anthem AI, United States","institution_ids":["https://openalex.org/I4210115830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391517","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-9599-8023","observed_orcids":["https://orcid.org/0000-0002-9599-8023"]},"institutions":[{"id":"https://openalex.org/I4210115830","display_name":"Anthem (United States)","ror":"https://ror.org/01geb0342","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["Anthem AI, Palo Alto, CA, United States","Anthem AI, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anthem AI, Palo Alto, CA, United States","institution_ids":[]},{"raw_affiliation_string":"Anthem AI, United States","institution_ids":["https://openalex.org/I4210115830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053036257","display_name":"Gino Tesei","orcid":"https://orcid.org/0000-0002-0882-5125","observed_orcids":["https://orcid.org/0000-0002-0882-5125"]},"institutions":[{"id":"https://openalex.org/I4210115830","display_name":"Anthem (United States)","ror":"https://ror.org/01geb0342","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gino Tesei","raw_affiliation_strings":["Anthem AI, Palo Alto, CA, United States","Anthem AI, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anthem AI, Palo Alto, CA, United States","institution_ids":[]},{"raw_affiliation_string":"Anthem AI, United States","institution_ids":["https://openalex.org/I4210115830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080060046","display_name":"Beau Norgeot","orcid":"https://orcid.org/0000-0003-2629-701X","observed_orcids":["https://orcid.org/0000-0003-2629-701X"]},"institutions":[{"id":"https://openalex.org/I4210115830","display_name":"Anthem (United States)","ror":"https://ror.org/01geb0342","country_code":"US","type":"company","lineage":["https://openalex.org/I4210115830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Beau Norgeot","raw_affiliation_strings":["Anthem AI, Palo Alto, CA, United States","Anthem AI, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Anthem AI, Palo Alto, CA, United States","institution_ids":[]},{"raw_affiliation_string":"Anthem AI, United States","institution_ids":["https://openalex.org/I4210115830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080060046"],"corresponding_institution_ids":["https://openalex.org/I4210115830"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":3.325,"has_fulltext":true,"cited_by_count":26,"citation_normalized_percentile":{"value":0.94469988,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"5","issue":null,"first_page":"918813","last_page":"918813"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.5884000062942505,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.5884000062942505,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.18019999563694,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.09200000017881393,"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/synthetic-patient-data","display_name":"synthetic patient data","score":0.9589999914169312},{"id":"https://openalex.org/keywords/privacy-preserving-data-generation","display_name":"privacy-preserving data generation","score":0.8349999785423279},{"id":"https://openalex.org/keywords/causal-effect-estimation","display_name":"causal effect estimation","score":0.9110000133514404},{"id":"https://openalex.org/keywords/multiple-treatments","display_name":"multiple treatments","score":0.9729999899864197},{"id":"https://openalex.org/keywords/hypertension","display_name":"hypertension","score":0.6769999861717224},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.7450000047683716},{"id":"https://openalex.org/keywords/wasserstein-distance","display_name":"Wasserstein distance","score":0.6809999942779541},{"id":"https://openalex.org/keywords/model-validation","display_name":"model validation","score":0.4729999899864197}],"concepts":[{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.7087435722351074},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6950252056121826},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6639828681945801},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5955531597137451},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5645685791969299},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5516798496246338},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5316356420516968},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5185590386390686},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.4472084939479828},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42789462208747864},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.418376624584198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4042106866836548},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38394081592559814},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.295642614364624},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28931188583374023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17896538972854614},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11942398548126221},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/frai.2022.918813","doi":"https://doi.org/10.3389/frai.2022.918813","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2022.918813","pdf_url":"https://www.frontiersin.org/articles/10.3389/frai.2022.918813/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:9515575","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9515575","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Front Artif Intell","raw_type":"Text"},{"id":"pmid:36187323","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36187323","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in artificial intelligence","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:78bfeac60aa4474a9d6e68fd26fdd98f","doi":null,"is_oa":true,"landing_page_url":"https://doaj.org/article/78bfeac60aa4474a9d6e68fd26fdd98f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Artificial Intelligence, Vol 5 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/frai.2022.918813","doi":"https://doi.org/10.3389/frai.2022.918813","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2022.918813","pdf_url":"https://www.frontiersin.org/articles/10.3389/frai.2022.918813/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.9537000060081482,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4297900505.pdf","grobid_xml":"https://content.openalex.org/works/W4297900505.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W48880850","https://openalex.org/W1975937116","https://openalex.org/W1987723939","https://openalex.org/W2000445173","https://openalex.org/W2035404753","https://openalex.org/W2055419092","https://openalex.org/W2064903582","https://openalex.org/W2066203299","https://openalex.org/W2096733369","https://openalex.org/W2105931767","https://openalex.org/W2137370054","https://openalex.org/W2148143831","https://openalex.org/W2150118316","https://openalex.org/W2150291618","https://openalex.org/W2165067028","https://openalex.org/W2396727790","https://openalex.org/W2548641835","https://openalex.org/W2560774707","https://openalex.org/W2580460079","https://openalex.org/W2604918751","https://openalex.org/W2605135824","https://openalex.org/W2620393362","https://openalex.org/W2739748921","https://openalex.org/W2751687090","https://openalex.org/W2789226849","https://openalex.org/W2889676056","https://openalex.org/W2908109788","https://openalex.org/W2913668833","https://openalex.org/W2961396908","https://openalex.org/W2996889063","https://openalex.org/W3012140936","https://openalex.org/W3022574011","https://openalex.org/W3103102495","https://openalex.org/W3103349836","https://openalex.org/W3109638816","https://openalex.org/W3120071807","https://openalex.org/W3122542817","https://openalex.org/W4233762729","https://openalex.org/W4236965008"],"related_works":["https://openalex.org/W4323841407","https://openalex.org/W7165927811","https://openalex.org/W4415967461","https://openalex.org/W7157203696","https://openalex.org/W3093150798","https://openalex.org/W7169877023","https://openalex.org/W4360615590","https://openalex.org/W7219335868","https://openalex.org/W7134828740","https://openalex.org/W3012140936"],"abstract_inverted_index":{"In":[0,117],"the":[1,11,26,45,73,212,218,229,234,262,289],"past":[2],"decade,":[3],"there":[4],"has":[5,40],"been":[6,41,62,85],"exponentially":[7],"growing":[8],"interest":[9],"in":[10,88,264,288,295],"use":[12],"of":[13,20,28,36,152,165,198,236,268,286],"observational":[14],"data":[15,71,159,174,204,247],"collected":[16],"as":[17,298],"a":[18,29,42,122,149,173,189,206,223],"part":[19],"routine":[21],"healthcare":[22],"practice":[23],"to":[24,65,80,102,211,216,251,283,293],"determine":[25],"effect":[27,266],"treatment":[30,193],"with":[31],"causal":[32,265],"inference":[33],"models.":[34],"Validation":[35],"these":[37,82],"models,":[38],"however,":[39],"challenge":[43],"because":[44],"ground":[46,74,130],"truth":[47,75,131],"is":[48,241],"unknown:":[49],"only":[50,107],"one":[51],"treatment-outcome":[52],"pair":[53],"for":[54,111,133,183,192,226],"each":[55,161],"person":[56],"can":[57,76,279],"be":[58,77,249,280],"observed.":[59],"There":[60],"have":[61,84],"multiple":[63],"efforts":[64],"fill":[66],"this":[67,118,227,273],"void":[68],"using":[69,272],"synthetic":[70,126,143,203,246],"where":[72],"generated.":[78],"However,":[79],"date,":[81],"datasets":[83,294],"severely":[86],"limited":[87],"their":[89],"utility":[90],"either":[91],"by":[92,147,177],"being":[93,100,239],"modeled":[94],"after":[95],"small":[96],"non-representative":[97],"patient":[98,158,185,213],"populations,":[99,105],"dissimilar":[101],"real":[103],"target":[104],"or":[106],"providing":[108],"known":[109],"effects":[110,132],"two":[112],"cohorts":[113],"(treated":[114],"vs.":[115],"control).":[116],"work,":[119],"we":[120,260,277],"produced":[121],"large-scale":[123],"and":[124,168,188,292],"realistic":[125],"dataset":[127,144],"that":[128,201,244],"provides":[129],"over":[134],"10":[135],"hypertension":[136,157],"treatments":[137],"on":[138],"blood":[139],"pressure":[140],"outcomes.":[141],"The":[142,275],"was":[145,222],"created":[146],"modeling":[148],"nationwide":[150],"cohort":[151,214],"more":[153],"than":[154],"580,":[155],"000":[156],"including":[160],"person's":[162],"multi-year":[163],"history":[164],"diagnoses,":[166],"medications,":[167],"laboratory":[169],"values.":[170],"We":[171],"designed":[172],"generation":[175,187],"process":[176],"combining":[178],"an":[179],"adapted":[180],"ADS-GAN":[181],"model":[182],"fictitious":[184],"information":[186],"neural":[190],"network":[191],"outcome":[194],"generation.":[195],"Wasserstein":[196],"distance":[197],"0.35":[199],"demonstrates":[200],"our":[202,245],"follows":[205],"nearly":[207],"identical":[208],"joint":[209],"distribution":[210],"used":[215,250,278],"generate":[217],"data.":[219],"Patient":[220],"privacy":[221],"primary":[224],"concern":[225],"study;":[228],"\u03f5-identifiability":[230],"metric,":[231],"which":[232],"estimates":[233],"probability":[235],"actual":[237,254],"patients":[238],"identified,":[240],"0.008%,":[242],"ensuring":[243],"cannot":[248],"identify":[252],"any":[253],"patients.":[255],"To":[256],"demonstrate":[257],"its":[258],"usage,":[259],"tested":[261],"bias":[263],"estimation":[267],"four":[269],"well-established":[270],"models":[271],"dataset.":[274],"approach":[276],"readily":[281],"extended":[282],"other":[284,296],"types":[285],"diseases":[287],"clinical":[290],"domain,":[291],"domains":[297],"well.":[299]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-10-01T00:00:00"}