{"id":"https://openalex.org/W7223000017","doi":"https://doi.org/10.64898/2026.10.18.26365858","title":"Long COVID undercoding and prediction from releasable electronic health record summaries in All of Us","display_name":"Long COVID undercoding and prediction from releasable electronic health record summaries in All of Us","publication_year":2026,"publication_date":"2026-10-20","ids":{"openalex":"https://openalex.org/W7223000017","doi":"https://doi.org/10.64898/2026.10.18.26365858"},"language":"en","primary_location":{"id":"doi:10.64898/2026.10.18.26365858","doi":"https://doi.org/10.64898/2026.10.18.26365858","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.10.18.26365858","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/10/20/2026.10.18.26365858.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.medrxiv.org/content/medrxiv/early/2026/10/20/2026.10.18.26365858.full.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034907795","display_name":"Amir Asiaee","orcid":"https://orcid.org/0000-0002-5317-9820","observed_orcids":["https://orcid.org/0000-0002-5317-9820"]},"institutions":[{"id":"https://openalex.org/I901861585","display_name":"Vanderbilt University Medical Center","ror":"https://ror.org/05dq2gs74","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210162197","https://openalex.org/I901861585"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Amir Asiaee","raw_affiliation_strings":["Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA"],"raw_orcid":"https://orcid.org/0000-0002-5317-9820","affiliations":[{"raw_affiliation_string":"Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA","institution_ids":["https://openalex.org/I901861585"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125234181","display_name":"Kaveh Aryan","orcid":"https://orcid.org/0009-0003-7245-4677","observed_orcids":["https://orcid.org/0009-0003-7245-4677"]},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kaveh Aryan","raw_affiliation_strings":["Department of Informatics, King\u2019s College London, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informatics, King\u2019s College London, London, UK","institution_ids":["https://openalex.org/I183935753"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034907795"],"corresponding_institution_ids":["https://openalex.org/I901861585"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11368","display_name":"Long-Term Effects of COVID-19","score":0.7675999999046326,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Clinical Neurology"},"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/T11368","display_name":"Long-Term Effects of COVID-19","score":0.7675999999046326,"subfield":{"id":"https://openalex.org/subfields/2728","display_name":"Clinical Neurology"},"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/T14400","display_name":"Medical Coding and Health Information","score":0.18310000002384186,"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":0.03999999910593033,"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/long-covid","display_name":"long COVID","score":0.9160000085830688},{"id":"https://openalex.org/keywords/undercoding","display_name":"undercoding","score":0.6230000257492065},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.871999979019165},{"id":"https://openalex.org/keywords/all-of-us-research-program","display_name":"All of Us","score":0.7540000081062317},{"id":"https://openalex.org/keywords/covid-19","display_name":"COVID-19 pandemic","score":0.7670000195503235},{"id":"https://openalex.org/keywords/household-pulse-survey","display_name":"Household Pulse Survey","score":0.6710000038146973},{"id":"https://openalex.org/keywords/disease-prevalence","display_name":"disease prevalence","score":0.36800000071525574},{"id":"https://openalex.org/keywords/predictive-modeling","display_name":"predictive modeling","score":0.36899998784065247},{"id":"https://openalex.org/keywords/naive-bayes","display_name":"naive Bayes","score":0.7310000061988831},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"linear discriminant analysis","score":0.9819999933242798},{"id":"https://openalex.org/keywords/elastic-net","display_name":"elastic net","score":0.984000027179718}],"concepts":[{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.744700014591217},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5555999875068665},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C3007834351","wikidata":"https://www.wikidata.org/wiki/Q82069695","display_name":"Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)","level":5,"score":0.4717000126838684},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4544000029563904},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.44909998774528503},{"id":"https://openalex.org/C3006700255","wikidata":"https://www.wikidata.org/wiki/Q81068910","display_name":"2019-20 coronavirus outbreak","level":3,"score":0.41769999265670776},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.4075999855995178},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.3846000134944916},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3747999966144562},{"id":"https://openalex.org/C138816342","wikidata":"https://www.wikidata.org/wiki/Q189603","display_name":"Public health","level":2,"score":0.3736000061035156},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2808000147342682},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.27810001373291016},{"id":"https://openalex.org/C2780154274","wikidata":"https://www.wikidata.org/wiki/Q7126717","display_name":"Download","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.2671000063419342},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.26420000195503235},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.250900000333786},{"id":"https://openalex.org/C198477413","wikidata":"https://www.wikidata.org/wiki/Q7647069","display_name":"Survey data collection","level":2,"score":0.250900000333786},{"id":"https://openalex.org/C76318530","wikidata":"https://www.wikidata.org/wiki/Q16833590","display_name":"Area under the curve","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.64898/2026.10.18.26365858","doi":"https://doi.org/10.64898/2026.10.18.26365858","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.10.18.26365858","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/10/20/2026.10.18.26365858.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.64898/2026.10.18.26365858","doi":"https://doi.org/10.64898/2026.10.18.26365858","is_oa":true,"landing_page_url":"https://doi.org/10.64898/2026.10.18.26365858","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2026/10/20/2026.10.18.26365858.full.pdf","source":{"id":"https://openalex.org/S3005729997","display_name":"medRxiv","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":["https://openalex.org/P4310315909"],"host_organization_lineage_names":["Cold Spring Harbor Laboratory Press"],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"medRxiv","raw_type":"posted-content"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.9537000060081482,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1497740631","display_name":"Illinois Precision Medicine Consortium ","funder_award_id":"OT2OD026557","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G1969433312","display_name":null,"funder_award_id":"AOD 16037","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G402197978","display_name":"Precision Medicine Initiative Cohort Program Biobank","funder_award_id":"U24OD023121","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G4546698633","display_name":"New York City Consortium for Precision Medicine","funder_award_id":"OT2OD026556","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5248101980","display_name":"Technology to Empower Changes in Health (TECH) Network Participant Technologies Center","funder_award_id":"U24OD023176","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G5558626780","display_name":"The New England Precision Medicine Consortium of the All of Us Research Program","funder_award_id":"OT2OD026553","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6088684187","display_name":"All of Us Wisconsin ","funder_award_id":"OT2OD026555","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6129883062","display_name":"Data and Research Support Center ","funder_award_id":"U2COD023196","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6256063357","display_name":"All of Us Pennsylvania","funder_award_id":"OT2OD026554","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6418186060","display_name":"Trans-America Consortium of the Health Care Systems Research Network for the All of Us Research Program","funder_award_id":"OT2OD026550","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G6936798765","display_name":"Southern All of Us Network","funder_award_id":"OT2OD026548","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G7835049221","display_name":"Adaptive Platform for Personalized Engagement","funder_award_id":"U24OD023163","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G8746466191","display_name":"California Precision Medicine Research Program Consortium","funder_award_id":"OT2OD026552","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223000017.pdf","grobid_xml":"https://content.openalex.org/works/W7223000017.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2000288463","https://openalex.org/W2123958887","https://openalex.org/W2128349406","https://openalex.org/W2157440718","https://openalex.org/W2276530525","https://openalex.org/W2587621812","https://openalex.org/W2899867782","https://openalex.org/W4205170775","https://openalex.org/W4280556295","https://openalex.org/W4320881351","https://openalex.org/W4377564798","https://openalex.org/W4382318708","https://openalex.org/W4385740434","https://openalex.org/W4386435258","https://openalex.org/W4392944512","https://openalex.org/W4394857110","https://openalex.org/W4400556444","https://openalex.org/W4410429629","https://openalex.org/W4412034766","https://openalex.org/W4413522525","https://openalex.org/W4415603057","https://openalex.org/W7124294713","https://openalex.org/W7141537467","https://openalex.org/W7160326720","https://openalex.org/W7161796055","https://openalex.org/W7164936309","https://openalex.org/W7165623169","https://openalex.org/W7167598324","https://openalex.org/W7170858042","https://openalex.org/W7223045909","https://openalex.org/W7223060380","https://openalex.org/W7223142728"],"related_works":["https://openalex.org/W4320712661","https://openalex.org/W4386435258","https://openalex.org/W4412034766","https://openalex.org/W4248158302","https://openalex.org/W4323528522","https://openalex.org/W4405961434","https://openalex.org/W4320881351","https://openalex.org/W3209155800","https://openalex.org/W4387380832","https://openalex.org/W4411207771"],"abstract_inverted_index":{"Abstract":[0],"Objective":[1],"To":[2],"estimate":[3],"Long":[4,129,224],"COVID":[5,130,225],"undercoding":[6],"in":[7,123],"electronic":[8],"health":[9],"records":[10],"(EHRs)":[11],"against":[12],"federal":[13],"survey":[14],"prevalence,":[15],"and":[16,35,74,84,97,109,134,165,228,231],"to":[17,93],"evaluate":[18],"a":[19,30,63,101,185,232],"predictor":[20],"of":[21,40,104],"later":[22,240],"coding":[23,137,198,221,241],"that":[24],"anyone":[25],"can":[26],"rebuild":[27],"from":[28,81,205,235],"counts":[29,83,207,237],"research":[31],"enclave":[32,80],"releases.":[33],"Materials":[34],"Methods":[36],"Among":[37,54],"23,790":[38],"All":[39],"Us":[41],"adults":[42,56,125],"with":[43,49,87,242],"COVID-19,":[44],"we":[45,61],"compared":[46,85],"coded":[47],"shares":[48],"Household":[50],"Pulse":[51],"Survey":[52],"prevalence.":[53],"23,300":[55],"uncoded":[57],"at":[58,168,200],"day":[59,68],"90,":[60],"predicted":[62],"first":[64],"U09.9":[65],"code":[66],"by":[67],"455.":[69],"We":[70],"fitted":[71,91],"naive":[72],"Bayes":[73],"linear":[75],"discriminant":[76],"models":[77],"outside":[78],"the":[79,94,135,150,162,174,178,181,189],"released":[82,146,206],"them":[86],"an":[88],"elastic":[89,175,182],"net":[90,183],"inside":[92],"same":[95],"patients":[96,105],"features.":[98],"Tests":[99],"were":[100,156],"random":[102],"20%":[103],"(4,680,":[106],"50":[107],"coded)":[108],"18":[110],"held-out":[111,169,201],"EHR":[112],"sites":[113],"(5,760,":[114],"60":[115],"coded).":[116],"Results":[117],"Under":[118],"survey-comparability":[119],"assumptions,":[120],"about":[121],"1":[122],"9":[124],"who":[126],"ever":[127],"had":[128,184],"carried":[131],"its":[132,239],"code,":[133],"women-to-men":[136],"ratio":[138],"was":[139],"0.79":[140],"(95%":[141,158],"sensitivity":[142],"interval,":[143],"0.65\u20130.95).":[144],"The":[145],"models\u2019":[147],"areas":[148],"under":[149,188,219],"receiver":[151],"operating":[152],"characteristic":[153],"curve":[154,191],"(AUROCs)":[155],"0.68":[157],"CI,":[159],"0.60\u20130.75)":[160],"on":[161],"patient":[163,179],"test":[164],"0.64":[166],"(0.55\u20130.72)":[167],"sites,":[170],"0.049":[171],"(0.001\u20130.104)":[172],"above":[173],"net.":[176],"On":[177],"test,":[180],"higher":[186],"area":[187],"precision-recall":[190],"(difference":[192],"0.062,":[193],"0.003\u20130.122).":[194],"Released":[195],"probabilities":[196],"overestimated":[197],"risk":[199],"sites.":[202],"Discussion":[203],"Fitting":[204],"did":[208],"not":[209],"consistently":[210],"lower":[211],"discrimination.":[212,244],"Coded":[213],"outcomes":[214],"inform":[215],"disease":[216],"discrimination":[217],"only":[218],"stated":[220],"assumptions.":[222],"Conclusion":[223],"is":[226],"substantially":[227],"unevenly":[229],"undercoded,":[230],"model":[233],"rebuilt":[234],"public":[236],"predicts":[238],"moderate":[243]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-10-21T00:00:00"}