{"id":"https://openalex.org/W7143299240","doi":"https://doi.org/10.48550/arxiv.2603.25923","title":"Preventing Data Leakage in EEG-Based Survival Prediction: A Two-Stage Embedding and Transformer Framework","display_name":"Preventing Data Leakage in EEG-Based Survival Prediction: A Two-Stage Embedding and Transformer Framework","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7143299240","doi":"https://doi.org/10.48550/arxiv.2603.25923"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25923","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25923","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130957878","display_name":"Yixin Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743103","display_name":"Zhixiang Liu","orcid":"https://orcid.org/0000-0001-6554-9617"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhixiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055251632","display_name":"Vladimir Zadorozhny","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zadorozhny, Vladimir I.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130920021","display_name":"Jonathan Elmer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elmer, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":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/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.7954000234603882,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/T10645","display_name":"Cardiac Arrest and Resuscitation","score":0.7954000234603882,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.1298999935388565,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.006300000008195639,"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/embedding","display_name":"Embedding","score":0.5900999903678894},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5885000228881836},{"id":"https://openalex.org/keywords/leakage","display_name":"Leakage (economics)","score":0.5587999820709229},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.5005000233650208},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.46639999747276306},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4438000023365021},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4345000088214874},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.397599995136261},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3928000032901764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.675599992275238},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5900999903678894},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5885000228881836},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.5587999820709229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5166000127792358},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.5005000233650208},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.46639999747276306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45190000534057617},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4438000023365021},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3928000032901764},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3756999969482422},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36640000343322754},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36550000309944153},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.3416000008583069},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C20892748","wikidata":"https://www.wikidata.org/wiki/Q4390394","display_name":"Magnetic flux leakage","level":3,"score":0.3084000051021576},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2833000123500824},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2630000114440918},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.25130000710487366},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25923","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25923","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25923","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6091518402099609,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2,45],"have":[3],"shown":[4],"promise":[5],"in":[6,74,178],"EEG-based":[7,202],"outcome":[8,203],"prediction":[9],"for":[10,200],"comatose":[11],"patients":[12,162],"after":[13],"cardiac":[14],"arrest,":[15],"but":[16],"their":[17],"reliability":[18],"is":[19],"often":[20],"compromised":[21],"by":[22],"subtle":[23],"forms":[24],"of":[25,71,160,191],"data":[26,72,193],"leakage.":[27],"In":[28,62,109,130],"particular,":[29],"when":[30],"long":[31],"EEG":[32,76,114,158],"recordings":[33],"are":[34,116],"segmented":[35],"into":[36,118],"short":[37,113],"windows":[38],"and":[39,49,59,170,195],"reused":[40],"across":[41],"multiple":[42],"training":[43,148],"stages,":[44],"may":[46],"implicitly":[47],"encode":[48],"propagate":[50],"label":[51],"information,":[52],"leading":[53],"to":[54,140,150],"overly":[55],"optimistic":[56],"validation":[57,89],"performance":[58,172],"poor":[60],"generalization.":[61],"this":[63,101],"study,":[64],"we":[65,103],"identify":[66],"a":[67,105,122,134,156,197],"previously":[68],"overlooked":[69],"form":[70],"leakage":[73,152],"multi-stage":[75],"modeling":[77],"pipelines.":[78],"We":[79],"demonstrate":[80],"that":[81,164],"violating":[82],"strict":[83,145],"patient-level":[84,142],"separation":[85],"can":[86],"significantly":[87],"inflate":[88],"metrics":[90],"while":[91],"causing":[92],"substantial":[93],"degradation":[94],"on":[95,155],"independent":[96],"test":[97],"data.":[98],"To":[99],"address":[100],"issue,":[102],"propose":[104],"leakage-aware":[106],"two-stage":[107],"framework.":[108],"the":[110,131,165,189],"first":[111],"stage,":[112,133],"segments":[115],"transformed":[117],"embedding":[119],"representations":[120],"using":[121],"convolutional":[123],"neural":[124],"network":[125],"with":[126,144],"an":[127],"ArcFace":[128],"objective.":[129],"second":[132],"Transformer-based":[135],"model":[136],"aggregates":[137],"these":[138],"embeddings":[139],"produce":[141],"predictions,":[143],"isolation":[146],"between":[147],"cohorts":[149],"eliminate":[151],"pathways.":[153],"Experiments":[154],"large-scale":[157],"dataset":[159],"post-cardiac-arrest":[161],"show":[163],"proposed":[166],"framework":[167],"achieves":[168],"stable":[169],"generalizable":[171],"under":[173],"clinically":[174],"relevant":[175],"constraints,":[176],"particularly":[177],"maintaining":[179],"high":[180],"sensitivity":[181],"at":[182],"stringent":[183],"specificity":[184],"thresholds.":[185],"These":[186],"results":[187],"highlight":[188],"importance":[190],"rigorous":[192],"partitioning":[194],"provide":[196],"practical":[198],"solution":[199],"reliable":[201],"prediction.":[204]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-31T00:00:00"}