{"id":"https://openalex.org/W7131407241","doi":"https://doi.org/10.48550/arxiv.2602.20442","title":"Imputation of Unknown Missingness in Sparse Electronic Health Records","display_name":"Imputation of Unknown Missingness in Sparse Electronic Health Records","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131407241","doi":"https://doi.org/10.48550/arxiv.2602.20442"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.20442","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126803685","display_name":"Jun Han","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050255836","display_name":"Josue Nassar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nassar, Josue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042529162","display_name":"Sanjit Singh Batra","orcid":"https://orcid.org/0000-0001-9849-865X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Batra, Sanjit Singh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126800139","display_name":"Aldo Cordova-Palomera","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cordova-Palomera, Aldo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042434776","display_name":"Vijay S. Nori","orcid":"https://orcid.org/0000-0002-3979-7577"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nori, Vijay","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5037895048","display_name":"Robert E. Tillman","orcid":"https://orcid.org/0000-0002-6586-119X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tillman, Robert E.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9233999848365784,"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"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9233999848365784,"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/T10136","display_name":"Statistical Methods and Inference","score":0.007799999788403511,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.007699999958276749,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.8794000148773193},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8202000260353088},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.46480000019073486},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.40709999203681946},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.37380000948905945},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.35370001196861267},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.3222000002861023},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.3212999999523163}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8794000148773193},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8202000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6866000294685364},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6014000177383423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5151000022888184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5012999773025513},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.46480000019073486},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3222000002861023},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.30230000615119934},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.20442","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.20442","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.20442","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.20442","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1],"holds":[2],"great":[3],"promise":[4],"for":[5,47,134],"advancing":[6],"the":[7,94,101,119,153,197],"field":[8],"of":[9,62,121],"medicine,":[10],"with":[11,100],"electronic":[12],"health":[13],"records":[14],"(EHRs)":[15],"serving":[16],"as":[17,57],"a":[18,83,105,113,130,147,179,207],"primary":[19],"data":[20,30,38,136,166],"source.":[21],"However,":[22],"EHRs":[23],"are":[24,167],"often":[25],"sparse":[26],"and":[27,35,40,188],"contain":[28],"missing":[29,49,58,84,140],"due":[31],"to":[32,76,137,158,184,190,206],"various":[33],"challenges":[34],"limitations":[36],"in":[37,87,142,161,174],"collection":[39],"sharing":[41],"between":[42],"healthcare":[43],"providers.":[44],"Existing":[45],"techniques":[46],"imputing":[48],"values":[50,61,141,160],"predominantly":[51],"focus":[52],"on":[53,193],"known":[54],"unknowns,":[55],"such":[56],"or":[59,103],"unavailable":[60],"lab":[63],"test":[64],"results;":[65],"most":[66],"do":[67],"not":[68,97,110],"explicitly":[69],"address":[70,125],"situations":[71,116],"where":[72,152,163],"it":[73],"is":[74,79,155],"difficult":[75],"distinguish":[77],"what":[78],"missing.":[80,168],"For":[81],"instance,":[82],"diagnosis":[85,106],"code":[86],"an":[88],"EHR":[89,181],"could":[90],"signify":[91],"either":[92],"that":[93,104],"patient":[95],"has":[96],"been":[98],"diagnosed":[99],"condition":[102],"was":[107],"made,":[108],"but":[109],"shared":[111],"by":[112],"provider.":[114],"Such":[115],"fall":[117],"into":[118],"paradigm":[120],"unknown":[122,139],"unknowns.":[123],"To":[124],"this":[126],"challenge,":[127],"we":[128,164],"develop":[129],"general":[131],"purpose":[132],"algorithm":[133],"denoising":[135,149,175],"recover":[138,159],"binary":[143],"EHRs.":[144],"We":[145],"design":[146],"transformer-based":[148],"neural":[150],"network":[151],"output":[154],"thresholded":[156],"adaptively":[157],"cases":[162],"predict":[165],"Our":[169],"results":[170],"demonstrate":[171],"improved":[172],"accuracy":[173],"medical":[176],"codes":[177],"within":[178],"real":[180,208],"dataset":[182],"compared":[183],"existing":[185,224],"imputation":[186],"approaches":[187],"leads":[189],"increased":[191],"performance":[192],"downstream":[194],"tasks":[195],"using":[196],"denoised":[198],"data.":[199],"In":[200],"particular,":[201],"when":[202],"applying":[203],"our":[204,216],"method":[205,217],"world":[209],"application,":[210],"predicting":[211],"hospital":[212],"readmission":[213],"from":[214],"EHRs,":[215],"achieves":[218],"statistically":[219],"significant":[220],"improvement":[221],"over":[222],"all":[223],"baselines.":[225]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-26T00:00:00"}
