{"id":"https://openalex.org/W7151815480","doi":"https://doi.org/10.48550/arxiv.2604.05116","title":"Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis","display_name":"Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151815480","doi":"https://doi.org/10.48550/arxiv.2604.05116"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.05116","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05116","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.05116","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133194839","display_name":"Xuyang Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Xuyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133201309","display_name":"Haoran Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133205619","display_name":"Dongjin Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Dongjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065576006","display_name":"Martin Renqiang Min","orcid":"https://orcid.org/0000-0002-8563-6133"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Min, Martin Renqiang","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":0.8780999779701233,"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":0.8780999779701233,"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/T12574","display_name":"Clinical Reasoning and Diagnostic Skills","score":0.03759999945759773,"subfield":{"id":"https://openalex.org/subfields/2714","display_name":"Family Practice"},"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/T10028","display_name":"Topic Modeling","score":0.021400000900030136,"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/benchmark","display_name":"Benchmark (surveying)","score":0.45239999890327454},{"id":"https://openalex.org/keywords/clinical-diagnosis","display_name":"Clinical diagnosis","score":0.4453999996185303},{"id":"https://openalex.org/keywords/diagnostic-test","display_name":"Diagnostic test","score":0.41999998688697815},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.3993000090122223},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.3723999857902527},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3465999960899353}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5392000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5131000280380249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5095999836921692},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45239999890327454},{"id":"https://openalex.org/C2983449737","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Clinical diagnosis","level":2,"score":0.4453999996185303},{"id":"https://openalex.org/C82157600","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic test","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3993000090122223},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C3020132585","wikidata":"https://www.wikidata.org/wiki/Q2671652","display_name":"Diagnostic accuracy","level":2,"score":0.34369999170303345},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C2989179672","wikidata":"https://www.wikidata.org/wiki/Q6806500","display_name":"Clinical decision making","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.2669000029563904}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.05116","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05116","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.05116","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.05116","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6638182997703552,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Clinical":[0],"diagnosis":[1,39,86,172],"requires":[2],"sequential":[3,44,85,170],"evidence":[4,30],"acquisition":[5],"under":[6,168],"uncertainty.":[7,150],"However,":[8],"most":[9],"Large":[10],"Language":[11],"Model":[12],"(LLM)":[13],"based":[14,95],"diagnostic":[15,54,78,103,108,111,130,145,166,177],"systems":[16],"assume":[17],"fully":[18],"observed":[19],"patient":[20],"information":[21,75],"and":[22,101,119],"therefore":[23],"do":[24],"not":[25],"explicitly":[26],"model":[27],"how":[28],"clinical":[29,69,171],"should":[31],"be":[32],"sequentially":[33],"acquired":[34],"over":[35],"time.":[36],"Even":[37],"when":[38],"is":[40,48,57,65,136],"formulated":[41],"as":[42,87,116],"a":[43,88,97,102,122,169],"decision":[45],"process,":[46],"it":[47],"still":[49],"challenging":[50],"to":[51,139],"learn":[52],"effective":[53],"trajectories.":[55],"This":[56],"because":[58],"the":[59,107,153,183],"space":[60],"of":[61,186],"possible":[62],"evidence-acquisition":[63],"paths":[64,118],"relatively":[66],"large,":[67],"while":[68,174],"datasets":[70],"rarely":[71],"provide":[72],"explicit":[73],"supervision":[74],"for":[76],"desirable":[77],"paths.":[79],"To":[80],"this":[81,141],"end,":[82],"we":[83,120],"formulate":[84],"Latent":[89],"Diagnostic":[90],"Trajectory":[91],"Learning":[92],"(LDTL)":[93],"framework":[94,161],"on":[96,152],"planning":[98,133],"LLM":[99,104,109,134],"agent":[100,135],"agent.":[105],"For":[106],"agent,":[110],"action":[112],"sequences":[113],"are":[114],"treated":[115],"latent":[117],"introduce":[121],"posterior":[123,188],"distribution":[124],"that":[125,147,157],"prioritizes":[126],"trajectories":[127,146],"providing":[128],"more":[129],"information.":[131],"The":[132],"then":[137],"trained":[138],"follow":[140],"distribution,":[142],"encouraging":[143],"coherent":[144],"progressively":[148],"reduce":[149],"Experiments":[151],"MIMIC-CDM":[154],"benchmark":[155],"demonstrate":[156],"our":[158],"proposed":[159],"LDTL":[160],"outperforms":[162],"existing":[163],"baselines":[164],"in":[165,190],"accuracy":[167],"setting,":[173],"requiring":[175],"fewer":[176],"tests.":[178],"Furthermore,":[179],"ablation":[180],"studies":[181],"highlight":[182],"critical":[184],"role":[185],"trajectory-level":[187],"alignment":[189],"achieving":[191],"these":[192],"improvements.":[193]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-09T00:00:00"}