{"id":"https://openalex.org/W7169716701","doi":"https://doi.org/10.1145/3770854.3780257","title":"EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning","display_name":"EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7169716701","doi":"https://doi.org/10.1145/3770854.3780257"},"language":null,"primary_location":{"id":"doi:10.1145/3770854.3780257","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3780257","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3770854.3780257","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5056039515","display_name":"Hengyu Zhang","orcid":"https://orcid.org/0009-0003-8100-2787"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hengyu Zhang","raw_affiliation_strings":["Macquarie University, Sydney, Australia"],"raw_orcid":"https://orcid.org/0009-0003-8100-2787","affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076120553","display_name":"Xuyun Zhang","orcid":"https://orcid.org/0000-0001-7353-4159"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuyun Zhang","raw_affiliation_strings":["Macquarie University, Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7353-4159","affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098008938","display_name":"Pengxiang Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengxiang Zhan","raw_affiliation_strings":["Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0009-0003-5675-0468","affiliations":[{"raw_affiliation_string":"Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007704896","display_name":"Linhao Luo","orcid":"https://orcid.org/0000-0003-0027-942X"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Linhao Luo","raw_affiliation_strings":["Monash University, Melbourne, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0027-942X","affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103160210","display_name":"Hang Lv","orcid":"https://orcid.org/0009-0007-2566-390X"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hang Lv","raw_affiliation_strings":["Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0009-0007-2566-390X","affiliations":[{"raw_affiliation_string":"Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083137715","display_name":"Yanchao Tan","orcid":"https://orcid.org/0000-0002-3526-6859"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanchao Tan","raw_affiliation_strings":["Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3526-6859","affiliations":[{"raw_affiliation_string":"Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008056593","display_name":"Shirui Pan","orcid":"https://orcid.org/0000-0003-0794-527X"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shirui Pan","raw_affiliation_strings":["Griffith University, Brisbane, Australia"],"raw_orcid":"https://orcid.org/0000-0003-0794-527X","affiliations":[{"raw_affiliation_string":"Griffith University, Brisbane, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006897094","display_name":"Carl Yang","orcid":"https://orcid.org/0000-0001-9145-4531"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carl Yang","raw_affiliation_strings":["Emory University, Atlanta, GA, USA"],"raw_orcid":"https://orcid.org/0000-0001-9145-4531","affiliations":[{"raw_affiliation_string":"Emory University, Atlanta, GA, USA","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"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":"1916","last_page":"1927"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7766000032424927},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.599399983882904},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5561000108718872},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.46389999985694885},{"id":"https://openalex.org/keywords/prioritization","display_name":"Prioritization","score":0.45559999346733093},{"id":"https://openalex.org/keywords/case-based-reasoning","display_name":"Case-based reasoning","score":0.3840999901294708}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7766000032424927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.710099995136261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70169997215271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6103000044822693},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5561000108718872},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.46389999985694885},{"id":"https://openalex.org/C2777615720","wikidata":"https://www.wikidata.org/wiki/Q11888847","display_name":"Prioritization","level":2,"score":0.45559999346733093},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3398999869823456},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.26759999990463257}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3770854.3780257","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3780257","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3770854.3780257","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3770854.3780257","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2171370925","https://openalex.org/W2557074642","https://openalex.org/W2690721124","https://openalex.org/W2757504960","https://openalex.org/W3004001630","https://openalex.org/W3080098168","https://openalex.org/W4322743452","https://openalex.org/W4322760726","https://openalex.org/W4367046802","https://openalex.org/W4391170193","https://openalex.org/W4391221150","https://openalex.org/W4392172846","https://openalex.org/W4393160078","https://openalex.org/W4396843823","https://openalex.org/W4401863414","https://openalex.org/W4402670791","https://openalex.org/W4405841623","https://openalex.org/W4409283601","https://openalex.org/W4409369188","https://openalex.org/W4413641530"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,11,109,156],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"have":[7],"enabled":[8],"promising":[9],"progress":[10],"diagnosis":[12,60,93,159],"prediction":[13],"from":[14],"electronic":[15],"health":[16],"records":[17],"(EHRs).":[18],"However,":[19],"existing":[20],"LLM-based":[21,59],"approaches":[22],"tend":[23],"to":[24,26,105],"overfit":[25],"historically":[27],"observed":[28],"diagnoses,":[29],"often":[30],"overlooking":[31],"novel":[32,92,158],"yet":[33],"clinically":[34],"important":[35],"conditions":[36],"that":[37,53,126],"are":[38,97,153],"critical":[39],"for":[40,77,83,91],"early":[41],"intervention.":[42],"To":[43],"address":[44],"this,":[45],"we":[46],"propose":[47],"EviCare,":[48],"an":[49,101,110,142],"in-context":[50,103],"reasoning":[51,108],"framework":[52],"integrates":[54],"deep":[55,74,138],"model":[56,75],"guidance":[57],"into":[58,100],"prediction.":[61,94],"Rather":[62],"than":[63],"prompting":[64],"LLMs":[65],"directly":[66],"with":[67],"raw":[68],"EHR":[69,120],"inputs,":[70],"EviCare":[71,127],"performs":[72],"(1)":[73],"inference":[76],"candidate":[78],"selection,":[79],"(2)":[80],"evidential":[81],"prioritization":[82],"set-based":[84],"EHRs,":[85],"and":[86,112,123,137,148],"(3)":[87],"relational":[88],"evidence":[89],"construction":[90],"These":[95],"signals":[96],"then":[98],"composed":[99],"adaptive":[102],"prompt":[104],"guide":[106],"LLM":[107],"accurate":[111],"interpretable":[113],"manner.":[114],"Extensive":[115],"experiments":[116],"on":[117],"two":[118],"real-world":[119],"benchmarks":[121],"(MIMIC-III":[122],"MIMIC-IV)":[124],"demonstrate":[125],"achieves":[128],"significant":[129],"performance":[130],"gains,":[131],"which":[132],"consistently":[133],"outperforms":[134],"both":[135],"LLM-only":[136],"model-only":[139],"baselines":[140],"by":[141],"average":[143,162],"of":[144,164],"20.65%":[145],"across":[146],"precision":[147],"accuracy":[149],"metrics.":[150],"The":[151],"improvements":[152,163],"particularly":[154],"notable":[155],"challenging":[157],"prediction,":[160],"yielding":[161],"30.97%.":[165]},"counts_by_year":[],"updated_date":"2026-07-20T07:56:41.581041","created_date":"2026-07-20T00:00:00"}
