{"id":"https://openalex.org/W7224667724","doi":"https://doi.org/10.1080/08839514.2026.2745885","title":"Early Prediction of Multiple Major Adverse Cardiovascular Events in Acute Coronary Syndrome: Comparison of Feature-Extraction Pipelines and Fine-Tuned Large Language Models","display_name":"Early Prediction of Multiple Major Adverse Cardiovascular Events in Acute Coronary Syndrome: Comparison of Feature-Extraction Pipelines and Fine-Tuned Large Language Models","publication_year":2026,"publication_date":"2026-10-29","ids":{"openalex":"https://openalex.org/W7224667724","doi":"https://doi.org/10.1080/08839514.2026.2745885"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2026.2745885","doi":"https://doi.org/10.1080/08839514.2026.2745885","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2026.2745885","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2745885?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2745885?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102828749","display_name":"Blanca V\u00e1zquez","orcid":"https://orcid.org/0000-0001-7708-8424","observed_orcids":["https://orcid.org/0000-0001-7708-8424"]},"institutions":[{"id":"https://openalex.org/I8961855","display_name":"Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25","country_code":"MX","type":"education","lineage":["https://openalex.org/I8961855"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Blanca Vazquez","raw_affiliation_strings":["Unidad Acad\u00e9mica del Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas (IIMAS) en el Estado de Yucat\u00e1n, Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM)"],"raw_orcid":"https://orcid.org/0000-0001-7708-8424","affiliations":[{"raw_affiliation_string":"Unidad Acad\u00e9mica del Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas (IIMAS) en el Estado de Yucat\u00e1n, Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM)","institution_ids":["https://openalex.org/I8961855"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008125439","display_name":"Gabriela Borrayo\u2013S\u00e1nchez","orcid":"https://orcid.org/0000-0001-8531-9923","observed_orcids":["https://orcid.org/0000-0001-8531-9923"]},"institutions":[{"id":"https://openalex.org/I8961855","display_name":"Universidad Nacional Aut\u00f3noma de M\u00e9xico","ror":"https://ror.org/01tmp8f25","country_code":"MX","type":"education","lineage":["https://openalex.org/I8961855"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Gabriela Borrayo-Sanchez","raw_affiliation_strings":["Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM)"],"raw_orcid":"https://orcid.org/0000-0001-8531-9923","affiliations":[{"raw_affiliation_string":"Universidad Nacional Aut\u00f3noma de M\u00e9xico (UNAM)","institution_ids":["https://openalex.org/I8961855"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102828749"],"corresponding_institution_ids":["https://openalex.org/I8961855"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82114253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"1","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":0.9965999722480774,"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":0.9965999722480774,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.002400000113993883,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/major-adverse-cardiovascular-events","display_name":"major adverse cardiovascular events","score":0.9419999718666077},{"id":"https://openalex.org/keywords/acute-coronary-syndrome","display_name":"acute coronary syndrome","score":0.9990000128746033},{"id":"https://openalex.org/keywords/st-elevation-myocardial-infarction","display_name":"ST-elevation myocardial infarction","score":0.9089999794960022},{"id":"https://openalex.org/keywords/non-st-elevation-myocardial-infarction","display_name":"non-ST-elevation myocardial infarction","score":0.996999979019165},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.7509999871253967},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.8100000023841858},{"id":"https://openalex.org/keywords/clinical-notes","display_name":"clinical notes","score":0.7049999833106995},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.7269999980926514},{"id":"https://openalex.org/keywords/risk-stratification","display_name":"risk stratification","score":0.6779999732971191},{"id":"https://openalex.org/keywords/in-hospital-mortality","display_name":"in-hospital mortality","score":0.8399999737739563},{"id":"https://openalex.org/keywords/recurrent-myocardial-infarction","display_name":"reinfarction","score":0.9480000138282776},{"id":"https://openalex.org/keywords/heart-failure","display_name":"heart failure","score":0.9300000071525574},{"id":"https://openalex.org/keywords/cardiogenic-shock","display_name":"cardiogenic shock","score":0.9959999918937683},{"id":"https://openalex.org/keywords/mimic-iii","display_name":"MIMIC-III","score":0.8259999752044678},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"multi-label classification","score":0.7179999947547913}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6312999725341797},{"id":"https://openalex.org/C500558357","wikidata":"https://www.wikidata.org/wiki/Q12152","display_name":"Myocardial infarction","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C2777698277","wikidata":"https://www.wikidata.org/wiki/Q266018","display_name":"Acute coronary syndrome","level":3,"score":0.5663999915122986},{"id":"https://openalex.org/C3020404979","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk stratification","level":2,"score":0.5206999778747559},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.4616999924182892},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43650001287460327},{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.376800000667572},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.3416999876499176},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.3301999866962433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3151000142097473},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3084999918937683},{"id":"https://openalex.org/C197934379","wikidata":"https://www.wikidata.org/wiki/Q2047938","display_name":"Adverse effect","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C36036425","wikidata":"https://www.wikidata.org/wiki/Q7846262","display_name":"Troponin","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.27959999442100525},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.2689000070095062},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2026.2745885","doi":"https://doi.org/10.1080/08839514.2026.2745885","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2026.2745885","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2745885?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:b2a9fbe450d1478a91ca9cbc094cd150","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/b2a9fbe450d1478a91ca9cbc094cd150","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 40, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2026.2745885","doi":"https://doi.org/10.1080/08839514.2026.2745885","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2026.2745885","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2026.2745885?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-scientific","cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-40","russia-white-list-2"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.979200005531311}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224667724.pdf","grobid_xml":"https://content.openalex.org/works/W7224667724.grobid-xml"},"referenced_works_count":70,"referenced_works":["https://openalex.org/W54887220","https://openalex.org/W1993014433","https://openalex.org/W2034690624","https://openalex.org/W2048456880","https://openalex.org/W2114198397","https://openalex.org/W2114995492","https://openalex.org/W2250539671","https://openalex.org/W2396881363","https://openalex.org/W2493916176","https://openalex.org/W2770266728","https://openalex.org/W2806587241","https://openalex.org/W2885523022","https://openalex.org/W2891400669","https://openalex.org/W2895166842","https://openalex.org/W2955547090","https://openalex.org/W2963341956","https://openalex.org/W2963716420","https://openalex.org/W2982424689","https://openalex.org/W3008576902","https://openalex.org/W3022371575","https://openalex.org/W3035391452","https://openalex.org/W3037458976","https://openalex.org/W3106196307","https://openalex.org/W3217469289","https://openalex.org/W4200231627","https://openalex.org/W4206045132","https://openalex.org/W4206491499","https://openalex.org/W4206678364","https://openalex.org/W4214738824","https://openalex.org/W4221134868","https://openalex.org/W4224267054","https://openalex.org/W4224279219","https://openalex.org/W4297253404","https://openalex.org/W4306670816","https://openalex.org/W4308419384","https://openalex.org/W4362544683","https://openalex.org/W4378219860","https://openalex.org/W4383067644","https://openalex.org/W4385965153","https://openalex.org/W4386120650","https://openalex.org/W4386780865","https://openalex.org/W4390588993","https://openalex.org/W4391138867","https://openalex.org/W4391188705","https://openalex.org/W4391232927","https://openalex.org/W4391340856","https://openalex.org/W4395676792","https://openalex.org/W4398173555","https://openalex.org/W4399199178","https://openalex.org/W4401337840","https://openalex.org/W4402671157","https://openalex.org/W4403320077","https://openalex.org/W4403806109","https://openalex.org/W4405304084","https://openalex.org/W4406002687","https://openalex.org/W4406062798","https://openalex.org/W4406152280","https://openalex.org/W4407354885","https://openalex.org/W4407588001","https://openalex.org/W4409838675","https://openalex.org/W4411330246","https://openalex.org/W4411369428","https://openalex.org/W4412594678","https://openalex.org/W4412649235","https://openalex.org/W4413571837","https://openalex.org/W4413784171","https://openalex.org/W4415367829","https://openalex.org/W4417157579","https://openalex.org/W6908700165","https://openalex.org/W7109123049"],"related_works":["https://openalex.org/W7118865744","https://openalex.org/W7116765014","https://openalex.org/W4416367929","https://openalex.org/W4414944485","https://openalex.org/W7125647611","https://openalex.org/W4386365447","https://openalex.org/W7164900925","https://openalex.org/W7163144098","https://openalex.org/W2520547729","https://openalex.org/W4318050008"],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"(LLMs)":[3],"have":[4],"shown":[5],"promise":[6],"for":[7,28,128,152],"supporting":[8],"clinical":[9,42,150],"decision-making":[10],"from":[11,44,53,68],"unstructured":[12],"health":[13],"records.":[14],"This":[15],"study":[16],"provides":[17],"a":[18,94],"systematic":[19],"comparison":[20],"of":[21,32,110,159],"feature-extraction":[22,102],"pipelines":[23],"and":[24,61,79,115,119,124,137,157],"end-to-end":[25,90],"fine-tuned":[26,91,144],"LLMs":[27,92,99,145],"the":[29,39,69],"early":[30,153],"prediction":[31,96],"major":[33],"adverse":[34],"cardiovascular":[35,154],"events":[36],"(MACE)":[37],"using":[38],"earliest":[40],"available":[41],"notes":[43,52],"patients":[45,55],"with":[46,56,83],"acute":[47],"coronary":[48],"syndrome":[49],"(ACS).":[50],"Clinical":[51],"2,650":[54],"ST-elevation":[57],"myocardial":[58,63],"infarction":[59,64],"(STEMI)":[60],"non-ST-elevation":[62],"(NSTEMI)":[65],"were":[66,87],"extracted":[67],"MIMIC-III":[70],"database.":[71],"Feature-extraction":[72],"approaches":[73],"based":[74],"on":[75],"TF-IDF,":[76],"word":[77],"embeddings,":[78],"transformer":[80],"representations":[81],"combined":[82],"machine":[84],"learning":[85],"classifiers":[86],"compared":[88],"against":[89],"in":[93,117,126],"multilabel":[95],"setting.":[97],"Fine-tuned":[98],"consistently":[100],"outperformed":[101],"pipelines.":[103],"The":[104],"best-performing":[105],"model":[106],"achieved":[107],"AUC":[108],"improvements":[109],"18.2%,":[111],"17.3%,":[112],"9.1%,":[113],"12.4%,":[114],"15.3%":[116],"STEMI,":[118],"17.2%,":[120],"7.5%,":[121],"9.2%,":[122],"14.5%,":[123],"10.0%":[125],"NSTEMI":[127],"predicting":[129],"in-hospital":[130],"mortality,":[131],"reinfarction,":[132],"heart":[133],"failure,":[134],"cardiogenic":[135],"shock,":[136],"MACE,":[138],"respectively.":[139],"These":[140],"findings":[141],"suggest":[142],"that":[143],"can":[146],"effectively":[147],"leverage":[148],"admission":[149],"narratives":[151],"risk":[155],"stratification":[156],"identification":[158],"high-risk":[160],"patients.":[161]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-30T00:00:00"}
