{"id":"https://openalex.org/W7131869894","doi":"https://doi.org/10.1186/s12911-026-03412-5","title":"Construction and comparison of preeclampsia prediction models using logistic regression and machine learning","display_name":"Construction and comparison of preeclampsia prediction models using logistic regression and machine learning","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7131869894","doi":"https://doi.org/10.1186/s12911-026-03412-5","pmid":"https://pubmed.ncbi.nlm.nih.gov/41761187"},"language":"en","primary_location":{"id":"doi:10.1186/s12911-026-03412-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12911-026-03412-5","pdf_url":null,"source":{"id":"https://openalex.org/S107516304","display_name":"BMC Medical Informatics and Decision Making","issn_l":"1472-6947","issn":["1472-6947"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Medical Informatics and Decision Making","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1186/s12911-026-03412-5","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127173361","display_name":"Jia Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Zhang","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127407623","display_name":"Hanbing Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanbing Jia","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127243611","display_name":"Tingting Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Zhang","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076391281","display_name":"Huanfang Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanfang Feng","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127147037","display_name":"Xiaoxuan Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxuan Zhu","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074495700","display_name":"Cuijuan Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuijuan Cao","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127231881","display_name":"Chunjing Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunjing Shi","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China","institution_ids":["https://openalex.org/I4210110525"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5127358498","display_name":"Yingjie Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110525","display_name":"Hebei General Hospital","ror":"https://ror.org/01nv7k942","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210110525"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yingjie Zhou","raw_affiliation_strings":["Hebei Reproductive Health Hospital, Shijiazhuang, China. yingjiehero@163.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hebei Reproductive Health Hospital, Shijiazhuang, China. yingjiehero@163.com","institution_ids":["https://openalex.org/I4210110525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5127358498"],"corresponding_institution_ids":["https://openalex.org/I4210110525"],"apc_list":{"value":1570,"currency":"GBP","value_usd":1925},"apc_paid":{"value":1570,"currency":"GBP","value_usd":1925},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25494614,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10290","display_name":"Pregnancy and preeclampsia studies","score":0.5267999768257141,"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"}},"topics":[{"id":"https://openalex.org/T10290","display_name":"Pregnancy and preeclampsia studies","score":0.5267999768257141,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.04529999941587448,"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/T11290","display_name":"Preterm Birth and Chorioamnionitis","score":0.02710000053048134,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/logistic-regression","display_name":"Logistic regression","score":0.8562999963760376},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6452999711036682},{"id":"https://openalex.org/keywords/brier-score","display_name":"Brier score","score":0.5512999892234802},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.48190000653266907},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4805000126361847},{"id":"https://openalex.org/keywords/preeclampsia","display_name":"Preeclampsia","score":0.46549999713897705},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.42329999804496765},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.3961000144481659},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3659000098705292}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.8562999963760376},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6452999711036682},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.6014999747276306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5587000250816345},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.5512999892234802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5029000043869019},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.48190000653266907},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C2777218350","wikidata":"https://www.wikidata.org/wiki/Q61335","display_name":"Preeclampsia","level":3,"score":0.46549999713897705},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37720000743865967},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C167135981","wikidata":"https://www.wikidata.org/wiki/Q2146302","display_name":"Retrospective cohort study","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32850000262260437},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.3197000026702881},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C2781381097","wikidata":"https://www.wikidata.org/wiki/Q5133843","display_name":"Clinical prediction rule","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C188816634","wikidata":"https://www.wikidata.org/wiki/Q2113324","display_name":"Prospective cohort study","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C2779234561","wikidata":"https://www.wikidata.org/wiki/Q11995","display_name":"Pregnancy","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C170964787","wikidata":"https://www.wikidata.org/wiki/Q7611170","display_name":"Stepwise regression","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C198433322","wikidata":"https://www.wikidata.org/wiki/Q3910099","display_name":"Predictive value of tests","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.2825999855995178},{"id":"https://openalex.org/C34626388","wikidata":"https://www.wikidata.org/wiki/Q721129","display_name":"Nomogram","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C131872663","wikidata":"https://www.wikidata.org/wiki/Q5284418","display_name":"Obstetrics","level":1,"score":0.2637999951839447},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.25049999356269836}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000093743","descriptor_name":"Random Forest","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000093743","descriptor_name":"Random Forest","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098404","descriptor_name":"Boosting Machine Learning Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098404","descriptor_name":"Boosting Machine Learning Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098412","descriptor_name":"Predictive Learning Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098412","descriptor_name":"Predictive Learning Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000328","descriptor_name":"Adult","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011225","descriptor_name":"Pre-Eclampsia","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D011225","descriptor_name":"Pre-Eclampsia","qualifier_ui":"Q000175","qualifier_name":"diagnosis","is_major_topic":true},{"descriptor_ui":"D011247","descriptor_name":"Pregnancy","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011247","descriptor_name":"Pregnancy","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012189","descriptor_name":"Retrospective Studies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016015","descriptor_name":"Logistic Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"doi:10.1186/s12911-026-03412-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12911-026-03412-5","pdf_url":null,"source":{"id":"https://openalex.org/S107516304","display_name":"BMC Medical Informatics and Decision Making","issn_l":"1472-6947","issn":["1472-6947"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Medical Informatics and Decision Making","raw_type":"journal-article"},{"id":"pmid:41761187","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41761187","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,"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":"BMC medical informatics and decision making","raw_type":null},{"id":"pmh:oai:doaj.org/article:67bbbbd076884672a81c18d7f201cb0e","is_oa":true,"landing_page_url":"https://doaj.org/article/67bbbbd076884672a81c18d7f201cb0e","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,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Medical Informatics and Decision Making, Vol 26, Iss 1 (2026)","raw_type":"article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13049806","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13049806/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Med Inform Decis Mak","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1186/s12911-026-03412-5","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12911-026-03412-5","pdf_url":null,"source":{"id":"https://openalex.org/S107516304","display_name":"BMC Medical Informatics and Decision Making","issn_l":"1472-6947","issn":["1472-6947"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Medical Informatics and Decision Making","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6315162777900696,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G7951122135","display_name":null,"funder_award_id":"20241706","funder_id":"https://openalex.org/F4320317799","funder_display_name":"Health Commission of Hebei Province"}],"funders":[{"id":"https://openalex.org/F4320317799","display_name":"Health Commission of Hebei Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2015327546","https://openalex.org/W2121290584","https://openalex.org/W2239180132","https://openalex.org/W2407973961","https://openalex.org/W2913997948","https://openalex.org/W2990033966","https://openalex.org/W3115448160","https://openalex.org/W3158846710","https://openalex.org/W3162825682","https://openalex.org/W3170054365","https://openalex.org/W4200359696","https://openalex.org/W4291158224","https://openalex.org/W4292002689","https://openalex.org/W4388014650","https://openalex.org/W4391609766","https://openalex.org/W4396229173","https://openalex.org/W4399072502","https://openalex.org/W4409351860"],"related_works":[],"abstract_inverted_index":{"OBJECTIVE:":[0],"Preeclampsia":[1],"is":[2,20,97],"a":[3,185,208],"major":[4],"cause":[5],"of":[6,15,91,117,126,129,171,175,179,196],"maternal":[7],"and":[8,11,25,48,80,111,133,142,177,217,222,233,244],"perinatal":[9],"morbidity":[10],"mortality.":[12],"Early":[13],"identification":[14],"women":[16],"at":[17],"high":[18],"risk":[19,210],"essential":[21],"for":[22,204],"timely":[23],"prevention":[24],"improved":[26],"outcomes.":[27],"METHODS:":[28],"In":[29,149],"this":[30,87,236],"study,":[31],"clinical":[32,250],"data":[33],"as":[34,36,102,184,207],"well":[35],"ultrasound":[37],"examination":[38],"results":[39],"from":[40,53],"404":[41],"pregnant":[42],"women,":[43],"among":[44],"whom":[45],"45":[46],"suffered":[47],"359":[49],"did":[50],"not":[51,241],"suffer":[52],"preeclampsia,":[54],"were":[55,60,146],"retrospectively":[56],"analyzed.":[57],"Independent":[58],"predictors":[59,232],"found":[61],"via":[62,99],"multivariable":[63],"logistic":[64,73,153],"regression.":[65],"With":[66],"these":[67,93],"predictors,":[68],"four":[69,94],"classification":[70,95],"models":[71,96,225],"involving":[72],"regression,":[74],"support":[75],"vector":[76],"machine,":[77],"random":[78,190],"forests,":[79],"extreme":[81],"gradient":[82],"boosting":[83],"are":[84],"developed":[85],"in":[86,235],"paper.":[88],"The":[89],"performance":[90],"all":[92],"assessed":[98],"metrics":[100],"such":[101],"area":[103],"under":[104],"receiver":[105],"operating":[106],"characteristic":[107],"curves,":[108],"calibration":[109,216],"plots,":[110],"decision":[112],"curves.":[113],"RESULTS:":[114],"Maternal":[115],"age":[116],"35":[118],"years":[119],"or":[120],"more,":[121],"assisted":[122],"reproductive":[123],"technology,":[124],"history":[125,128],"eclampsia,":[127],"fetal":[130],"growth":[131],"restriction,":[132],"two":[134],"placental":[135],"blood":[136],"flow":[137,140,144],"indices":[138],"-":[139],"index":[141],"vascular":[143],"index,":[145],"independent":[147],"predictors.":[148],"the":[150,158,193],"internal":[151],"validations,":[152],"regression":[154,200],"was":[155],"characterized":[156],"by":[157],"most":[159,202],"favorable":[160,215],"calibrated":[161],"method,":[162],"based":[163],"on":[164],"10-fold":[165],"out-of-fold":[166],"prediction,":[167],"with":[168],"an":[169],"intercept":[170],"\u2212":[172],"0.325,":[173],"slope":[174],"0.811,":[176],"Brier":[178],"0.090,":[180],"suggesting":[181],"its":[182,214],"suitability":[183],"probability-based":[186,209],"predictive":[187],"tool,":[188],"while":[189],"forest":[191],"produced":[192],"highest":[194],"AUC":[195],"0.781.":[197],"CONCLUSIONS:":[198],"Logistic":[199],"appears":[201],"suitable":[203],"early":[205],"screening":[206],"tool":[211],"due":[212],"to":[213],"interpretability,":[218],"pending":[219],"external":[220,246],"validation":[221,247],"recalibration.":[223],"These":[224],"quantify":[226],"statistical":[227],"associations":[228],"between":[229],"routinely":[230],"collected":[231],"preeclampsia":[234],"retrospective":[237],"cohort;":[238],"they":[239],"do":[240],"imply":[242],"causality":[243],"require":[245],"before":[248],"prospective":[249],"use.":[251]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2026-02-28T00:00:00"}
