{"id":"https://openalex.org/W7160494498","doi":"https://doi.org/10.48550/arxiv.2605.04071","title":"FlatASCEND: Autoregressive Clinical Sequence Generation with Continuous Time Prediction and Association-Based Pharmacological Testing","display_name":"FlatASCEND: Autoregressive Clinical Sequence Generation with Continuous Time Prediction and Association-Based Pharmacological Testing","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7160494498","doi":"https://doi.org/10.48550/arxiv.2605.04071"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04071","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04071","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112250373","display_name":"Chris Sainsbury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sainsbury, Chris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135568785","display_name":"Feng Dong","orcid":"https://orcid.org/0009-0005-2941-5706"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Feng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026210150","display_name":"Andreas Karwath","orcid":"https://orcid.org/0000-0002-6942-3760"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karwath, Andreas","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":0.9642999768257141,"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":0.9642999768257141,"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":0.0038999998942017555,"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":0.0032999999821186066,"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/autoregressive-model","display_name":"Autoregressive model","score":0.7351999878883362},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.4740999937057495},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.46709999442100525},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.45829999446868896},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.4526999890804291},{"id":"https://openalex.org/keywords/fidelity","display_name":"Fidelity","score":0.4244999885559082},{"id":"https://openalex.org/keywords/concordance","display_name":"Concordance","score":0.41780000925064087},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.41110000014305115},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4108999967575073}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7351999878883362},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4887000024318695},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.46709999442100525},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C160798450","wikidata":"https://www.wikidata.org/wiki/Q4230870","display_name":"Concordance","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3921999931335449},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3887999951839447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3801000118255615},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.3677999973297119},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3474999964237213},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C206041023","wikidata":"https://www.wikidata.org/wiki/Q1751970","display_name":"Wilcoxon signed-rank test","level":3,"score":0.3375999927520752},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3294999897480011},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.328900009393692},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04071","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04071","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04071","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":"Preprint"},"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":{"Autoregressive":[0],"models":[1],"can":[2],"predict":[3],"clinical":[4,35],"events,":[5],"but":[6],"generating":[7],"patient-conditioned":[8],"multi-step":[9],"trajectories":[10],"that":[11],"respond":[12],"to":[13,83,86,96,158],"intervention":[14],"tokens":[15,41],"and":[16,42,165,191],"testing":[17],"whether":[18],"those":[19],"responses":[20],"preserve":[21],"known":[22],"pharmacological":[23,78,106],"associations":[24,91,143,156],"has":[25],"received":[26],"limited":[27],"attention.":[28],"We":[29],"present":[30],"FlatASCEND,":[31],"a":[32,43],"14.5M-parameter":[33],"autoregressive":[34],"sequence":[36],"model":[37],"using":[38],"flat":[39],"composite":[40],"zero-inflated":[44],"log-normal":[45],"time":[46],"head.":[47],"Standard":[48],"distributional":[49],"metrics":[50],"(Jaccard":[51],"0.889-0.954)":[52],"do":[53],"not":[54],"distinguish":[55],"FlatASCEND":[56],"from":[57,67],"trivial":[58],"baselines;":[59],"the":[60],"model's":[61],"value":[62],"lies":[63],"in":[64],"conditional":[65],"generation":[66],"patient-specific":[68,74],"prefixes.":[69],"A":[70],"prompt-shuffle":[71],"ablation":[72],"shows":[73],"conditioning":[75],"amplifies":[76],"mechanistic":[77,116],"effects":[79],"(2.0-2.2x":[80],"for":[81,94,175],"steroid":[82],"glucose,":[84],"diuretic":[85],"potassium)":[87],"while":[88],"leaving":[89],"confounding-driven":[90],"unchanged":[92],"(0.9x":[93],"insulin":[95],"glucose).":[97],"An":[98],"incident-user":[99],"framework":[100],"assesses":[101],"directional":[102],"consistency":[103],"against":[104],"prior":[105],"knowledge":[107],"on":[108,189],"MIMIC-IV":[109],"(N=500":[110],"per":[111],"comparison):":[112],"4/10":[113],"recover":[114],"correct":[115,155],"directions,":[117],"2":[118],"reproduce":[119],"treatment-context":[120],"associations,":[121],"4":[122],"are":[123],"incorrect":[124],"(9/10":[125],"significant,":[126],"Wilcoxon":[127],"p<0.05).":[128],"This":[129],"pattern":[130],"-":[131,137],"partial":[132],"recovery":[133],"under":[134],"residual":[135],"confounding":[136],"is":[138,173,182],"consistent":[139],"with":[140,150],"learned":[141],"observational":[142],"without":[144,196],"causal":[145],"distinction.":[146],"Direct":[147],"preference":[148],"optimisation":[149],"surrogate":[151],"reward":[152,161,164],"destroys":[153],"all":[154],"(3/3":[157],"0/3),":[159],"illustrating":[160],"exploitation":[162],"when":[163],"evaluation":[166],"share":[167],"an":[168],"outcome":[169],"domain.":[170],"Generative":[171],"evidence":[172],"strongest":[174],"short-horizon":[176],"ICU":[177],"data;":[178],"outpatient":[179],"temporal":[180],"fidelity":[181],"weaker":[183],"(median":[184],"10":[185],"vs":[186],"154":[187],"days":[188],"INSPECT),":[190],"zero-shot":[192],"cross-site":[193],"transfer":[194],"degrades":[195],"adaptation.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}