{"id":"https://openalex.org/W7160426581","doi":"https://doi.org/10.48550/arxiv.2605.03570","title":"Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data","display_name":"Disentangling Shared and Task-Specific Representations from Multi-Modal Clinical Data","publication_year":2026,"publication_date":"2026-05-05","ids":{"openalex":"https://openalex.org/W7160426581","doi":"https://doi.org/10.48550/arxiv.2605.03570"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.03570","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03570","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":null,"license_id":null,"version":null,"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":"https://doi.org/10.48550/arxiv.2605.03570","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135419903","display_name":"He Lyu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lyu, He","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089649617","display_name":"Huolin Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Huolin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135531671","display_name":"Junren Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Junren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033812192","display_name":"Huazhen Yang","orcid":"https://orcid.org/0000-0003-4515-1446"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Huazhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048742182","display_name":"Linchao He","orcid":"https://orcid.org/0000-0002-0562-3026"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Linchao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135530262","display_name":"Yong Chen","orcid":"https://orcid.org/0000-0002-8377-5914"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049899766","display_name":"Zhirui Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhirui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135442149","display_name":"Andreas Maier","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maier, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053698756","display_name":"Siming Bayer","orcid":"https://orcid.org/0000-0003-2874-4805"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bayer, Siming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135529216","display_name":"Huan Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Huan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":10,"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":0.9223999977111816,"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":0.9223999977111816,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.027799999341368675,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.011500000022351742,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"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/redundancy","display_name":"Redundancy (engineering)","score":0.6254000067710876},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5526000261306763},{"id":"https://openalex.org/keywords/orthogonality","display_name":"Orthogonality","score":0.49129998683929443},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.47679999470710754},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4397999942302704},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.39419999718666077},{"id":"https://openalex.org/keywords/data-redundancy","display_name":"Data redundancy","score":0.38999998569488525},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.36739999055862427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.704800009727478},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6254000067710876},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5526000261306763},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5443999767303467},{"id":"https://openalex.org/C17137986","wikidata":"https://www.wikidata.org/wiki/Q215067","display_name":"Orthogonality","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4397999942302704},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43689998984336853},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.39419999718666077},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.36149999499320984},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.31209999322891235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.310699999332428},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.25459998846054077},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.03570","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03570","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.03570","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.03570","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-world":[0],"clinical":[1,169,187],"data":[2],"is":[3],"inherently":[4],"multimodal,":[5],"providing":[6],"complementary":[7],"evidence":[8],"that":[9,174],"mirrors":[10],"the":[11,129,141],"practical":[12],"necessity":[13],"of":[14,115,134,144],"jointly":[15],"assessing":[16],"multiple":[17],"related":[18],"outcomes.":[19,122],"Although":[20],"multi-task":[21,70,151],"learning":[22,39],"can":[23,46,181],"improve":[24,182],"efficiency":[25],"by":[26],"sharing":[27,45,56],"information":[28],"across":[29],"outcomes,":[30],"existing":[31],"approaches":[32],"often":[33],"fail":[34],"to":[35,86,101],"balance":[36],"shared":[37,60,91,177],"representation":[38],"with":[40,81],"outcome-specific":[41],"modeling.":[42],"Hard":[43],"parameter":[44],"trigger":[47],"negative":[48],"transfer":[49],"when":[50],"task":[51],"gradients":[52],"conflict,":[53],"while":[54],"flexible":[55],"may":[57],"still":[58],"entangle":[59],"and":[61,92,95,104,136,150,178],"task-specific":[62,93,106,179],"signals.":[63,107],"To":[64],"address":[65],"this,":[66],"we":[67],"propose":[68],"a":[69,74,97,112],"framework":[71],"built":[72],"on":[73,111],"unified":[75],"Transformer":[76],"for":[77,119],"multimodal":[78,186],"fusion,":[79],"augmented":[80],"Orthogonal":[82],"Task":[83],"Decomposition":[84],"(OrthTD)":[85],"split":[87],"patient":[88],"representations":[89,180],"into":[90],"subspaces":[94],"impose":[96],"geometric":[98],"orthogonality":[99],"constraint":[100],"reduce":[102],"redundancy":[103],"isolate":[105],"We":[108],"evaluated":[109],"OrthTD":[110,123,154],"real-world":[113],"cohort":[114],"12,430":[116],"surgical":[117],"patients":[118],"predicting":[120],"four":[121],"achieved":[124],"average":[125,137],"AUC":[126],"(area":[127,139],"under":[128,140],"receiver":[130],"operating":[131],"characteristic":[132],"curve)":[133,143],"87.5%":[135],"AUPRC":[138],"precision-recall":[142],"37.2%,":[145],"consistently":[146],"outperformed":[147],"advanced":[148],"tabular":[149],"methods.":[152],"Notably,":[153],"achieves":[155],"substantial":[156],"gains":[157],"in":[158,163],"AUPRC,":[159],"indicating":[160],"superior":[161],"performance":[162],"identifying":[164],"rare":[165],"events":[166],"within":[167],"imbalanced":[168],"data.":[170,188],"These":[171],"results":[172],"suggest":[173],"enforcing":[175],"non-redundant":[176],"multi-outcome":[183],"prediction":[184],"from":[185]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-07T00:00:00"}