{"id":"https://openalex.org/W7155040845","doi":"https://doi.org/10.48550/arxiv.2604.17725","title":"RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models","display_name":"RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155040845","doi":"https://doi.org/10.48550/arxiv.2604.17725"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.17725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17725","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.17725","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015103614","display_name":"Arya Hadizadeh Moghaddam","orcid":"https://orcid.org/0000-0003-0935-4756"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moghaddam, Arya Hadizadeh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134181335","display_name":"Drew Ross","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ross, Drew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092710033","display_name":"Mohsen Nayebi Kerdabadi","orcid":"https://orcid.org/0009-0007-5729-1565"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kerdabadi, Mohsen Nayebi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134186126","display_name":"Dongjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Dongjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134200431","display_name":"Zijun Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Zijun","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/T13702","display_name":"Machine Learning in Healthcare","score":0.9887999892234802,"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":0.9887999892234802,"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":0.004800000227987766,"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":0.002300000051036477,"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/inference","display_name":"Inference","score":0.6901999711990356},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6718000173568726},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6481000185012817},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6014999747276306},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5924000144004822},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5598999857902527},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.4066999852657318},{"id":"https://openalex.org/keywords/electronic-health-record","display_name":"Electronic health record","score":0.39959999918937683},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.390500009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.741599977016449},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6901999711990356},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6718000173568726},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6481000185012817},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6014999747276306},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5924000144004822},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5598999857902527},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5134999752044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5044000148773193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4544999897480011},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.4066999852657318},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.39959999918937683},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.36980000138282776},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.3544999957084656},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C2777601897","wikidata":"https://www.wikidata.org/wiki/Q3409113","display_name":"Presentation (obstetrics)","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30489999055862427},{"id":"https://openalex.org/C46503548","wikidata":"https://www.wikidata.org/wiki/Q1145976","display_name":"Plain text","level":3,"score":0.301800012588501},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.28839999437332153},{"id":"https://openalex.org/C2910417920","wikidata":"https://www.wikidata.org/wiki/Q4116434","display_name":"Patient privacy","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C206497026","wikidata":"https://www.wikidata.org/wiki/Q1753883","display_name":"SNOMED CT","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26980000734329224},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C2777083192","wikidata":"https://www.wikidata.org/wiki/Q1814648","display_name":"Plain language","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C3018822202","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient data","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C21338462","wikidata":"https://www.wikidata.org/wiki/Q1662581","display_name":"Information model","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.17725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17725","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,"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.17725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17725","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,"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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5708088278770447,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"shown":[5],"strong":[6],"promise":[7],"for":[8,28],"mining":[9],"Electronic":[10],"Health":[11],"Records":[12],"(EHRs)":[13],"by":[14],"reasoning":[15],"over":[16],"longitudinal":[17,65,134],"clinical":[18,168],"information":[19,139],"to":[20,60,132],"capture":[21,61],"context-rich":[22],"patient":[23,92],"trajectories.":[24],"However,":[25],"leveraging":[26,97],"LLMs":[27,81],"structured":[29,113],"EHRs":[30],"(e.g.,":[31],"standardized":[32],"diagnosis":[33],"and":[34,54,64,136,154,163],"medication":[35],"codes)":[36],"presents":[37],"two":[38],"key":[39],"challenges.":[40],"First,":[41],"translating":[42],"time-stamped":[43],"EHR":[44,114,149],"sequences":[45],"into":[46],"plain":[47],"text":[48],"can":[49],"obscure":[50],"both":[51,161],"temporal":[52],"structure":[53],"code":[55,62],"identities,":[56],"weakening":[57],"the":[58],"ability":[59],"co-occurrence":[63],"regularities.":[66],"Second,":[67],"unlike":[68],"cohort-trained":[69],"predictive":[70],"models":[71],"that":[72,111,157],"learn":[73],"a":[74,86,107,146],"shared,":[75],"task-aligned":[76,148],"representation":[77],"space":[78],"across":[79,166],"patients,":[80],"are":[82],"often":[83],"applied":[84],"in":[85],"case-isolated":[87],"inference":[88],"setting":[89],"where":[90],"each":[91],"is":[93],"processed":[94],"independently":[95],"without":[96,119],"population-level":[98,138],"patterns.":[99],"To":[100],"address":[101],"these":[102],"challenges,":[103],"we":[104],"introduce":[105],"RePrompT,":[106],"time-aware":[108],"LLM":[109],"framework":[110],"integrates":[112],"encoders":[115],"through":[116,140],"prompt":[117,142],"tuning,":[118],"modifying":[120],"underlying":[121],"architectures.":[122],"Specifically,":[123],"RePrompT":[124,158],"recurrently":[125],"incorporates":[126],"latent":[127],"states":[128],"from":[129,145],"prior":[130],"visits":[131],"preserve":[133],"information,":[135],"injects":[137],"trainable":[141],"tokens":[143],"derived":[144],"cohort-trained,":[147],"encoder.":[150],"Experiments":[151],"on":[152],"MIMIC-III":[153],"MIMIC-IV":[155],"demonstrate":[156],"consistently":[159],"outperforms":[160],"EHR-based":[162],"LLM-based":[164],"baselines":[165],"multiple":[167],"prediction":[169],"tasks.":[170]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-22T00:00:00"}