{"id":"https://openalex.org/W7204159153","doi":"https://doi.org/10.48550/arxiv.2608.22583","title":"Clinical Graph-JEPA: Predictive Patient-State Knowledge Graphs for Cognitive Decision Support","display_name":"Clinical Graph-JEPA: Predictive Patient-State Knowledge Graphs for Cognitive Decision Support","publication_year":2026,"publication_date":"2026-08-23","ids":{"openalex":"https://openalex.org/W7204159153","doi":"https://doi.org/10.48550/arxiv.2608.22583"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.22583","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22583","pdf_url":"https://arxiv.org/pdf/2608.22583","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.22583","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148314438","display_name":"Kushagra Yadav","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yadav, Kushagra","raw_affiliation_strings":["New York University ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University ,","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148166092","display_name":"Nalin Prabhath","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prabhath, Nalin","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5153531599","display_name":"Amit Lamba","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lamba, Amit","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003246998","display_name":"James E. Schrager","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Schrager, James E.","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101183795","display_name":"Goeun Han","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Han, Goeun","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5148436239","display_name":"Yining Mao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mao, Yining","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.95169997215271,"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":0.95169997215271,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.023800000548362732,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.010300000198185444,"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/clinical-knowledge-graphs","display_name":"clinical knowledge graphs","score":0.9639999866485596},{"id":"https://openalex.org/keywords/jepa","display_name":"JEPA","score":0.7540000081062317},{"id":"https://openalex.org/keywords/graph-refinement","display_name":"graph refinement","score":0.45500001311302185},{"id":"https://openalex.org/keywords/mimic-iv","display_name":"MIMIC-IV","score":0.7310000061988831},{"id":"https://openalex.org/keywords/discharge-notes","display_name":"discharge notes","score":0.7910000085830688},{"id":"https://openalex.org/keywords/knowledge-graph-construction","display_name":"knowledge graph construction","score":0.5379999876022339},{"id":"https://openalex.org/keywords/clinical-decision-support","display_name":"clinical decision support","score":0.765999972820282}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6572999954223633},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.6528000235557556},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5034000277519226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4514999985694885},{"id":"https://openalex.org/C63527458","wikidata":"https://www.wikidata.org/wiki/Q5133829","display_name":"Clinical decision support system","level":3,"score":0.4489000141620636},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3822999894618988},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3564000129699707},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.3529999852180481},{"id":"https://openalex.org/C25810664","wikidata":"https://www.wikidata.org/wiki/Q44325","display_name":"Ontology","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31929999589920044},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.2992999851703644},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27230000495910645},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C234837","wikidata":"https://www.wikidata.org/wiki/Q1420493","display_name":"Conceptual graph","level":3,"score":0.258899986743927},{"id":"https://openalex.org/C2778373050","wikidata":"https://www.wikidata.org/wiki/Q1774706","display_name":"Clinical pathway","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.22583","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22583","pdf_url":"https://arxiv.org/pdf/2608.22583","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.22583","doi":"https://doi.org/10.48550/arxiv.2608.22583","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.22583","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.22583","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22583","pdf_url":"https://arxiv.org/pdf/2608.22583","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5683000087738037,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204159153.pdf","grobid_xml":"https://content.openalex.org/works/W7204159153.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2949311246","https://openalex.org/W3187966659","https://openalex.org/W4297808394","https://openalex.org/W4313439128","https://openalex.org/W4386076428","https://openalex.org/W4412886823","https://openalex.org/W4416124930"],"related_works":["https://openalex.org/W4383045310","https://openalex.org/W7213884738","https://openalex.org/W4390889688","https://openalex.org/W2086113695","https://openalex.org/W7113905442","https://openalex.org/W7119083784","https://openalex.org/W3208335349","https://openalex.org/W4285145947","https://openalex.org/W2796786478","https://openalex.org/W3208182228"],"abstract_inverted_index":{"Clinical":[0],"records":[1,90],"contain":[2],"rich":[3],"evidence":[4,11,52],"about":[5],"patient":[6],"state,":[7],"but":[8],"converting":[9],"that":[10,44,142],"into":[12,31,148],"reliable,":[13],"structured":[14,88],"knowledge":[15,38,63],"graphs":[16],"remains":[17],"difficult":[18],"because":[19],"extraction":[20,68],"errors,":[21],"ontology":[22],"mismatch,":[23],"missing":[24],"relations,":[25],"and":[26,41,54,91,117,119,124,155],"temporal":[27],"ambiguity":[28],"can":[29],"propagate":[30],"downstream":[32],"systems.":[33],"We":[34,107],"propose":[35],"a":[36,61,66,74,135,139],"clinical":[37,62,93,100],"graph":[39,64,86,105],"construction":[40],"refinement":[42],"framework":[43],"combines":[45],"multi-agent":[46],"relation":[47],"proposal,":[48],"ontology-aware":[49],"normalization,":[50],"deterministic":[51],"scoring,":[53],"JEPA-based":[55],"latent":[56],"refinement.":[57],"Rather":[58],"than":[59],"treating":[60],"as":[65,73],"static":[67],"artifact,":[69],"we":[70,133],"treat":[71],"it":[72],"predictive":[75],"patient-state":[76],"representation.":[77],"For":[78],"each":[79],"admission,":[80],"the":[81,103,109,128,152],"system":[82],"constructs":[83],"an":[84],"evidence-scored":[85],"from":[87,102],"MIMIC-IV":[89],"inferred":[92],"cross-links,":[94],"then":[95],"learns":[96],"to":[97],"recover":[98],"held-out":[99,120],"relations":[101],"observed":[104],"context.":[106],"evaluate":[108],"refiner":[110],"with":[111,138],"leakage-free":[112],"leave-one-out":[113,163],"edge":[114],"recovery":[115],"(MRR":[116],"Hits@k)":[118],"batch-mask":[121],"evaluation":[122,156],"(AUC":[123],"MRR).":[125],"To":[126],"isolate":[127],"contribution":[129],"of":[130],"discharge-note":[131,145],"context,":[132],"compare":[134],"note-embedding-free":[136],"configuration":[137,141],"note-augmented":[140],"injects":[143],"real":[144],"representations":[146],"only":[147],"note-grounded":[149],"entities.":[150],"Under":[151],"same":[153],"cohort":[154],"protocol,":[157],"entity-grounded":[158],"note":[159],"injection":[160],"improves":[161],"overall":[162],"MRR":[164],"by":[165],"31%":[166],"relative":[167],"improvement.":[168]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-26T00:00:00"}