{"id":"https://openalex.org/W7224093965","doi":"https://doi.org/10.1002/ijgo.71376","title":"Integrating large language models and expert knowledge to identify risk factors and predict mode of delivery with machine learning","display_name":"Integrating large language models and expert knowledge to identify risk factors and predict mode of delivery with machine learning","publication_year":2026,"publication_date":"2026-10-28","ids":{"openalex":"https://openalex.org/W7224093965","doi":"https://doi.org/10.1002/ijgo.71376","pmid":"https://pubmed.ncbi.nlm.nih.gov/42949299"},"language":"en","primary_location":{"id":"doi:10.1002/ijgo.71376","doi":"https://doi.org/10.1002/ijgo.71376","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ijgo.71376","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijgo.71376","source":{"id":"https://openalex.org/S4210216427","display_name":"International Journal of Gynecology & Obstetrics","issn_l":"0020-6695","issn":["0020-6695","0020-7292","1879-3479"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Gynecology &amp; Obstetrics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijgo.71376","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136149526","display_name":"Farzaneh Rashidi","orcid":"https://orcid.org/0000-0001-7497-4180","observed_orcids":["https://orcid.org/0000-0001-7497-4180"]},"institutions":[{"id":"https://openalex.org/I4210159363","display_name":"North Khorasan University of Medical Sciences","ror":"https://ror.org/0536t7y80","country_code":"IR","type":"education","lineage":["https://openalex.org/I4210159363"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Farzaneh Rashidi","raw_affiliation_strings":["Clinical Research Development Unit Bentolhoda Hospital, North Khorasan University of Medical Sciences  Bojnurd Iran"],"raw_orcid":"https://orcid.org/0000-0001-7497-4180","affiliations":[{"raw_affiliation_string":"Clinical Research Development Unit Bentolhoda Hospital, North Khorasan University of Medical Sciences  Bojnurd Iran","institution_ids":["https://openalex.org/I4210159363"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159995814","display_name":"Saba Qanbarzadeh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210159363","display_name":"North Khorasan University of Medical Sciences","ror":"https://ror.org/0536t7y80","country_code":"IR","type":"education","lineage":["https://openalex.org/I4210159363"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Saba Qanbarzadeh","raw_affiliation_strings":["Clinical Research Development Unit Bentolhoda Hospital, North Khorasan University of Medical Sciences  Bojnurd Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clinical Research Development Unit Bentolhoda Hospital, North Khorasan University of Medical Sciences  Bojnurd Iran","institution_ids":["https://openalex.org/I4210159363"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009488392","display_name":"Azam Orooji","orcid":"https://orcid.org/0000-0002-2773-6179","observed_orcids":["https://orcid.org/0000-0002-2773-6179","https://orcid.org/0000-0003-0278-3275"]},"institutions":[{"id":"https://openalex.org/I4210159363","display_name":"North Khorasan University of Medical Sciences","ror":"https://ror.org/0536t7y80","country_code":"IR","type":"education","lineage":["https://openalex.org/I4210159363"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Azam Orooji","raw_affiliation_strings":["Department of Medical Biotechnology, School of Medicine North Khorasan University of Medical Sciences  Bojnurd Iran"],"raw_orcid":"https://orcid.org/0000-0002-2773-6179","affiliations":[{"raw_affiliation_string":"Department of Medical Biotechnology, School of Medicine North Khorasan University of Medical Sciences  Bojnurd Iran","institution_ids":["https://openalex.org/I4210159363"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5009488392"],"corresponding_institution_ids":["https://openalex.org/I4210159363"],"apc_list":{"value":4480,"currency":"USD","value_usd":4480},"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87752809,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.6586999893188477,"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"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.6586999893188477,"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/T10453","display_name":"Maternal and Perinatal Health Interventions","score":0.31619998812675476,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0142000000923872,"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/mode-of-delivery-prediction","display_name":"mode of delivery prediction","score":0.9869999885559082},{"id":"https://openalex.org/keywords/cesarean-section","display_name":"cesarean section","score":0.8489999771118164},{"id":"https://openalex.org/keywords/vaginal-delivery","display_name":"vaginal delivery","score":0.9240000247955322},{"id":"https://openalex.org/keywords/risk-factors","display_name":"risk factors","score":0.6679999828338623},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9829999804496765},{"id":"https://openalex.org/keywords/hybrid-feature-selection","display_name":"hybrid feature selection","score":0.7149999737739563},{"id":"https://openalex.org/keywords/expert-knowledge-integration","display_name":"expert knowledge integration","score":0.7839999794960022},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8610000014305115},{"id":"https://openalex.org/keywords/previous-cesarean-section","display_name":"previous cesarean section","score":0.9150000214576721},{"id":"https://openalex.org/keywords/fetal-presentation","display_name":"fetal presentation","score":0.8989999890327454},{"id":"https://openalex.org/keywords/types-of-insurance","display_name":"insurance type","score":0.8159999847412109}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.8140000104904175},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6801000237464905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6671000123023987},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6340000033378601},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5781000256538391},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5726000070571899},{"id":"https://openalex.org/C63527458","wikidata":"https://www.wikidata.org/wiki/Q5133829","display_name":"Clinical decision support system","level":3,"score":0.558899998664856},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.49900001287460327},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.46700000762939453},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.45249998569488525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4153999984264374},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.31060001254081726},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C34626388","wikidata":"https://www.wikidata.org/wiki/Q721129","display_name":"Nomogram","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.289000004529953},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1002/ijgo.71376","doi":"https://doi.org/10.1002/ijgo.71376","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ijgo.71376","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijgo.71376","source":{"id":"https://openalex.org/S4210216427","display_name":"International Journal of Gynecology & Obstetrics","issn_l":"0020-6695","issn":["0020-6695","0020-7292","1879-3479"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Gynecology &amp; Obstetrics","raw_type":"journal-article"},{"id":"pmid:42949299","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42949299","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1002/ijgo.71376","doi":"https://doi.org/10.1002/ijgo.71376","is_oa":true,"landing_page_url":"https://doi.org/10.1002/ijgo.71376","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ijgo.71376","source":{"id":"https://openalex.org/S4210216427","display_name":"International Journal of Gynecology & Obstetrics","issn_l":"0020-6695","issn":["0020-6695","0020-7292","1879-3479"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Gynecology &amp; Obstetrics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":1.0,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[{"display_name":"Observational Study","id":"https://openalex.org/study-designs/observational-study"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224093965.pdf","grobid_xml":"https://content.openalex.org/works/W7224093965.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W429766147","https://openalex.org/W1953222123","https://openalex.org/W1963669771","https://openalex.org/W2010508199","https://openalex.org/W2134906196","https://openalex.org/W2140535499","https://openalex.org/W2145443732","https://openalex.org/W2188856651","https://openalex.org/W2792919287","https://openalex.org/W2808237135","https://openalex.org/W2896867593","https://openalex.org/W2914980345","https://openalex.org/W3026942373","https://openalex.org/W3083390848","https://openalex.org/W3111676751","https://openalex.org/W3139098840","https://openalex.org/W3166989839","https://openalex.org/W4211032164","https://openalex.org/W4211051919","https://openalex.org/W4221110970","https://openalex.org/W4224279211","https://openalex.org/W4243072198","https://openalex.org/W4280505593","https://openalex.org/W4308735737","https://openalex.org/W4318205408","https://openalex.org/W4376871625","https://openalex.org/W4382344377","https://openalex.org/W4385298618","https://openalex.org/W4400369505","https://openalex.org/W4404707649","https://openalex.org/W4408115307"],"related_works":["https://openalex.org/W4410319678","https://openalex.org/W4390507082","https://openalex.org/W3111676751","https://openalex.org/W6893273880","https://openalex.org/W4281763158","https://openalex.org/W4411727115","https://openalex.org/W4406417091","https://openalex.org/W4402996948","https://openalex.org/W4226417694","https://openalex.org/W7215903514"],"abstract_inverted_index":{"BACKGROUND:":[0],"Accurately":[1],"predicting":[2],"the":[3,32,65,79,173,188,207,240,259,294],"mode":[4,66,85],"of":[5,34,67,130,182,191,227,232],"delivery":[6,68,84,299],"is":[7],"a":[8,45],"significant":[9],"challenge":[10],"in":[11,102],"obstetrics,":[12],"with":[13,217,225,248,281,301],"implications":[14],"for":[15,143],"maternal":[16],"healthcare":[17],"planning,":[18],"resource":[19],"allocation,":[20],"and":[21,37,47,55,76,128,133,156,184,203,229,244,253,267,288],"personalized":[22],"patient":[23],"counseling.":[24],"While":[25],"machine":[26,73,159],"learning":[27,74,146],"offers":[28],"promising":[29],"predictive":[30,165,211],"capabilities,":[31],"selection":[33,292],"relevant":[35],"clinical":[36,56],"socioeconomic":[38],"risk":[39,81,120,285],"factors":[40,82],"from":[41],"vast":[42],"possibilities":[43],"remains":[44],"critical":[46],"nontrivial":[48],"step":[49],"that":[50,277,289],"directly":[51],"affects":[52],"model":[53,296],"performance":[54,175,224],"utility.":[57],"OBJECTIVES:":[58],"This":[59,274],"study":[60,90,275],"aimed":[61],"to":[62,139,163,297],"(1)":[63],"predict":[64,298],"(vaginal":[69],"vs.":[70],"cesarean)":[71],"using":[72,176],"algorithms":[75],"(2)":[77],"identify":[78],"key":[80],"influencing":[83],"prediction.":[86],"METHODS:":[87],"A":[88,105],"retrospective":[89],"was":[91,110,137],"conducted":[92],"on":[93],"eligible":[94],"pregnant":[95],"women":[96,216,247],"who":[97],"delivered":[98],"at":[99],"Bent-Al-Huda":[100],"Hospital":[101],"Bojnourd,":[103],"Iran.":[104],"hybrid":[106],"feature":[107,291],"identification":[108,287],"approach":[109],"employed:":[111],"large":[112],"language":[113],"models":[114],"(LLMs)":[115],"initially":[116],"proposed":[117,295],"63":[118],"potential":[119],"factors.":[121,212],"Through":[122],"iterative":[123],"refinement":[124],"involving":[125],"domain":[126],"experts":[127],"assessment":[129],"data":[131],"availability":[132],"frequency,":[134],"this":[135],"list":[136],"consolidated":[138],"35":[140],"final":[141],"variables":[142],"modeling.":[144],"Machine":[145],"algorithms,":[147],"including":[148],"multilayer":[149],"perceptron":[150],"(MLP),":[151],"decision":[152],"tree,":[153],"na\u00efve":[154],"Bayes,":[155],"support":[157],"vector":[158],"(SVM),":[160],"were":[161],"applied":[162],"build":[164],"models.":[166],"RESULTS:":[167],"Among":[168],"all":[169,177],"models,":[170],"SVM":[171,221,256],"showed":[172,258],"best":[174],"features,":[178],"achieving":[179],"an":[180,185,230],"accuracy":[181,226,242,268],"94.8%":[183],"area":[186],"under":[187],"curve":[189],"(AUC)":[190],"0.94.":[192],"Decision":[193],"tree":[194],"analysis":[195],"identified":[196],"previous":[197,249],"cesarean":[198,219,250],"section":[199,251],"history,":[200],"insurance":[201],"type,":[202],"fetal":[204],"presentation":[205],"as":[206],"three":[208],"most":[209],"important":[210],"After":[213],"excluding":[214,246],"40":[215],"prior":[218],"delivery,":[220],"maintained":[222],"robust":[223],"94.3%":[228],"AUC":[231,265,270],"0.92.":[233],"Using":[234],"antenatal":[235],"features":[236],"only,":[237],"MLP":[238],"achieved":[239],"highest":[241,260],"before":[243],"after":[245],"(92.9%":[252],"92.2%),":[254],"while":[255],"also":[257],"discriminative":[261],"ability":[262],"(accuracy":[263],"92.4%,":[264],"0.90":[266],"91.3%,":[269],"0.87,":[271],"respectively).":[272],"CONCLUSION:":[273],"demonstrates":[276],"combining":[278],"expert":[279],"knowledge":[280],"LLMs":[282],"enables":[283],"effective":[284],"factor":[286],"careful":[290],"allows":[293],"type":[300],"high":[302],"accuracy.":[303]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-29T00:00:00"}
