{"id":"https://openalex.org/W7140378448","doi":"https://doi.org/10.48550/arxiv.2603.24562","title":"Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction","display_name":"Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140378448","doi":"https://doi.org/10.48550/arxiv.2603.24562"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.24562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24562","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.2603.24562","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067129067","display_name":"Haresh Rengaraj Rajamohan","orcid":"https://orcid.org/0000-0002-7926-9090"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajamohan, Haresh Rengaraj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130695720","display_name":"Xiang Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130658297","display_name":"Weicheng Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Weicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025534919","display_name":"Shih\u2010Lun Huang","orcid":"https://orcid.org/0000-0002-1157-3758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Shih-Lun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130689105","display_name":"Long Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130658276","display_name":"Gabe Schulman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schulman, Gabe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130698584","display_name":"Huizhen Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Huizhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130656239","display_name":"Shengduo Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shengduo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130707574","display_name":"Yixuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130717566","display_name":"Huidi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Huidi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130703700","display_name":"Kyunghyun Cho","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cho, Kyunghyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050471723","display_name":"Cem M. Deniz","orcid":"https://orcid.org/0000-0001-8809-5945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deniz, Cem M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5079997593","display_name":"Narges Razavian","orcid":"https://orcid.org/0000-0002-9922-6370"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Razavian, Narges","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":13,"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":0.989799976348877,"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":0.989799976348877,"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/T10028","display_name":"Topic Modeling","score":0.003000000026077032,"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":0.0020000000949949026,"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/event-data","display_name":"Event data","score":0.48190000653266907},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.42480000853538513},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.3991999924182892},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.38940000534057617},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.38749998807907104},{"id":"https://openalex.org/keywords/diagnosis-code","display_name":"Diagnosis code","score":0.3840999901294708},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.37450000643730164},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3725999891757965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6833000183105469},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.48190000653266907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48080000281333923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4440999925136566},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4350999891757965},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.3840999901294708},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3650999963283539},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.35120001435279846},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3393000066280365},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3239000141620636},{"id":"https://openalex.org/C154874363","wikidata":"https://www.wikidata.org/wiki/Q3518464","display_name":"Medical classification","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3190000057220459},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.27489998936653137},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2694000005722046},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.24562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24562","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.2603.24562","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.24562","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"large-scale":[1],"pretraining":[2,25],"has":[3],"revolutionized":[4],"language":[5,159],"modeling,":[6],"its":[7],"potential":[8],"remains":[9],"underexplored":[10],"in":[11,75,103,119],"healthcare":[12],"with":[13],"structured":[14],"electronic":[15],"health":[16],"records":[17],"(EHRs).":[18],"We":[19,63,122],"present":[20],"RAVEN,":[21],"a":[22,38,72,104,134,155],"novel":[23],"generative":[24],"strategy":[26],"for":[27,55,129],"sequential":[28],"EHR":[29],"data":[30,120],"based":[31],"on":[32,60,66],"Recurrence-Aware":[33],"next-Visit":[34],"EveNt":[35],"prediction.":[36],"Leveraging":[37],"dataset":[39],"of":[40,133,137],"over":[41],"one":[42],"million":[43],"unique":[44],"individuals,":[45],"our":[46,124],"model":[47,78,112,125,160],"learns":[48],"to":[49,173],"autoregressively":[50],"generate":[51],"tokenized":[52],"clinical":[53,180],"events":[54,69],"the":[56,100,131],"next":[57],"visit":[58],"conditioned":[59],"patient":[61,176],"history.":[62],"introduce":[64],"regularization":[65],"predicting":[67],"repeated":[68,80],"and":[70,147,154,183],"highlight":[71],"key":[73],"pitfall":[74],"EHR-based":[76],"foundation":[77],"evaluations:":[79],"event":[81],"tokens":[82],"can":[83,171],"inflate":[84],"performance":[85],"metrics":[86],"when":[87],"new":[88],"onsets":[89],"are":[90],"not":[91],"distinguished":[92],"from":[93],"subsequent":[94],"occurrences.":[95],"Furthermore,":[96],"we":[97,167],"empirically":[98],"investigate":[99],"scaling":[101],"behaviors":[102],"data-constrained,":[105],"compute-saturated":[106],"regime,":[107],"showing":[108],"that":[109,169],"simply":[110],"increasing":[111],"size":[113],"is":[114],"suboptimal":[115],"without":[116,163],"commensurate":[117],"increases":[118],"volume.":[121],"evaluate":[123],"via":[126],"zero-shot":[127],"prediction":[128],"forecasting":[130],"incidence":[132],"diverse":[135],"set":[136],"diseases,":[138],"where":[139],"it":[140],"rivals":[141],"fully":[142],"fine-tuned":[143],"representation-based":[144],"Transformer":[145],"models":[146],"outperforms":[148],"both":[149],"standard":[150],"simulation-based":[151],"next-token":[152],"approaches":[153],"prompted":[156],"medical":[157],"large":[158],"baseline.":[161],"Finally,":[162],"additional":[164],"parameter":[165],"updates,":[166],"show":[168],"RAVEN":[170],"generalize":[172],"an":[174],"external":[175],"cohort":[177],"under":[178],"lossy":[179],"code":[181],"mappings":[182],"feature":[184],"coverage":[185],"gaps.":[186]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-27T00:00:00"}