{"id":"https://openalex.org/W2944651223","doi":"https://doi.org/10.1097/01.aog.0000558957.93957.8d","title":"Predictive Modeling of Spontaneous Preterm Birth Using Machine Learning [36C]","display_name":"Predictive Modeling of Spontaneous Preterm Birth Using Machine Learning [36C]","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2944651223","doi":"https://doi.org/10.1097/01.aog.0000558957.93957.8d","mag":"2944651223"},"language":"en","primary_location":{"id":"doi:10.1097/01.aog.0000558957.93957.8d","doi":"https://doi.org/10.1097/01.aog.0000558957.93957.8d","is_oa":false,"landing_page_url":"https://doi.org/10.1097/01.aog.0000558957.93957.8d","pdf_url":null,"source":{"id":"https://openalex.org/S139838620","display_name":"Obstetrics and Gynecology","issn_l":"0029-7844","issn":["0029-7844","1873-233X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","medline","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315671","host_organization_name":"Lippincott Williams & Wilkins","host_organization_lineage":["https://openalex.org/P4310315671","https://openalex.org/P4310318547"],"host_organization_lineage_names":["Lippincott Williams & Wilkins","Wolters Kluwer"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Obstetrics &amp; Gynecology","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076340246","display_name":"Stephanie A. Guang","orcid":"https://orcid.org/0000-0001-6763-5279","observed_orcids":["https://orcid.org/0000-0001-6763-5279"]},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephanie Guang","raw_affiliation_strings":["The Warren Alpert Medical School of Brown University, Providence, RI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Warren Alpert Medical School of Brown University, Providence, RI","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086602697","display_name":"Indra Neil Sarkar","orcid":"https://orcid.org/0000-0003-2054-7356","observed_orcids":["https://orcid.org/0000-0003-2054-7356"]},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Indra N. Sarkar","raw_affiliation_strings":["The Warren Alpert Medical School of Brown University, Providence, RI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Warren Alpert Medical School of Brown University, Providence, RI","institution_ids":["https://openalex.org/I27804330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015534543","display_name":"Erika F. Werner","orcid":"https://orcid.org/0000-0002-0938-582X","observed_orcids":["https://orcid.org/0000-0002-0938-582X"]},"institutions":[{"id":"https://openalex.org/I27804330","display_name":"Brown University","ror":"https://ror.org/05gq02987","country_code":"US","type":"education","lineage":["https://openalex.org/I27804330"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erika F. Werner","raw_affiliation_strings":["The Warren Alpert Medical School of Brown University, Providence, RI"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Warren Alpert Medical School of Brown University, Providence, RI","institution_ids":["https://openalex.org/I27804330"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27804330"],"apc_list":null,"apc_paid":null,"fwci":0.8217,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.79007966,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"133","issue":"1","first_page":"41S","last_page":"41S"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9452999830245972,"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":0.9452999830245972,"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":0.0364999994635582,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.008999999612569809,"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/spontaneous-preterm-birth","display_name":"spontaneous preterm birth","score":1.0},{"id":"https://openalex.org/keywords/preterm-birth-prediction","display_name":"preterm birth prediction","score":0.8970000147819519},{"id":"https://openalex.org/keywords/risk-prediction","display_name":"risk prediction","score":0.6620000004768372},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.6990000009536743},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8370000123977661},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9120000004768372},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.9309999942779541},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.9330000281333923},{"id":"https://openalex.org/keywords/administrative-health-data","display_name":"administrative health data","score":0.7620000243186951},{"id":"https://openalex.org/keywords/comorbidity","display_name":"comorbidities","score":0.7149999737739563}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.8339614272117615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7660397291183472},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7260150909423828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6832752227783203},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6270927786827087},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.6042948961257935},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.586051881313324},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5762296915054321},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5510554313659668},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5206484198570251},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.4917795956134796},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4896877110004425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.14416828751564026},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.10803702473640442},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1097/01.aog.0000558957.93957.8d","doi":"https://doi.org/10.1097/01.aog.0000558957.93957.8d","is_oa":false,"landing_page_url":"https://doi.org/10.1097/01.aog.0000558957.93957.8d","pdf_url":null,"source":{"id":"https://openalex.org/S139838620","display_name":"Obstetrics and Gynecology","issn_l":"0029-7844","issn":["0029-7844","1873-233X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-a","doyens","jufo-3","ki-jl-2","medline","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315671","host_organization_name":"Lippincott Williams & Wilkins","host_organization_lineage":["https://openalex.org/P4310315671","https://openalex.org/P4310318547"],"host_organization_lineage_names":["Lippincott Williams & Wilkins","Wolters Kluwer"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Obstetrics &amp; Gynecology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.9922999739646912}],"study_designs":[{"id":"https://openalex.org/study-designs/observational-study","display_name":"Observational Study"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4292342904","https://openalex.org/W4412362982","https://openalex.org/W4416949951","https://openalex.org/W4403599279","https://openalex.org/W7005330379","https://openalex.org/W4402660826","https://openalex.org/W4404471410","https://openalex.org/W7167251567","https://openalex.org/W4415565254","https://openalex.org/W4392292716"],"abstract_inverted_index":{"INTRODUCTION:":[0],"Spontaneous":[1],"preterm":[2],"birth":[3],"(sPTB)":[4],"is":[5],"the":[6,20,44,93,147,153,170,176,195,203],"clinical":[7,77,248],"manifestation":[8],"of":[9,22,29,90,99,105,142,211,250],"many":[10],"overlapping":[11],"pathologies,":[12],"making":[13],"its":[14],"prediction":[15],"challenging.":[16],"This":[17],"study":[18,41],"explored":[19],"potential":[21],"machine":[23,126],"learning":[24,217,236],"techniques":[25],"to":[26,76,169],"identify":[27],"risk":[28],"sPTB":[30,160,223,251],"using":[31,113,146,228],"complex,":[32],"non-linear":[33],"associations":[34],"between":[35,96],"variables.":[36],"METHODS:":[37],"Data":[38,137],"for":[39,65,84,189,247],"this":[40],"came":[42],"from":[43,231],"Healthcare":[45],"Cost":[46],"and":[47,70,79,122,129,140,166,187,194,206,209,213],"Utilization":[48],"Project":[49],"National":[50],"Inpatient":[51],"Sample":[52],"database,":[53],"which":[54],"included":[55],"835,704":[56,154],"obstetric":[57,155],"patients.":[58],"Subjects":[59],"were":[60,82,144,164,182],"divided":[61],"into":[62],"three":[63],"cohorts":[64],"training":[66,171],"(60%),":[67],"validation":[68],"(20%),":[69],"testing":[71,141],"(20%).":[72],"Thirty-one":[73],"features,":[74],"relating":[75],"diagnoses":[78],"demographic":[80],"data,":[81],"selected":[83],"developing":[85],"predictive":[86],"models.":[87],"The":[88,173,198],"goal":[89],"classification":[91],"was":[92,111],"binary":[94],"discrimination":[95],"spontaneous":[97],"onset":[98],"delivery":[100],"before":[101],"37":[102],"completed":[103],"weeks":[104],"gestation":[106],"versus":[107,224],"full-term":[108],"birth.":[109],"Classification":[110],"performed":[112,200],"logistic":[114],"regression":[115],"(LR),":[116],"random":[117],"forest":[118],"classifier":[119],"(RFC),":[120],"C":[121],"nu":[123],"support":[124],"vector":[125],"classifiers":[127,143],"(C/nu-SVC),":[128],"a":[130,207,245],"multi-layer":[131],"perceptron":[132],"artificial":[133],"neural":[134],"network":[135],"(ANN).":[136],"processing,":[138],"training,":[139],"done":[145],"Julia":[148],"programming":[149],"language.":[150],"RESULTS:":[151],"Of":[152],"patients,":[156],"49,563":[157],"resulted":[158],"in":[159,221],"(5.9%).":[161],"Classifier":[162],"models":[163],"constructed":[165],"assessed":[167],"relative":[168],"dataset.":[172],"areas":[174],"under":[175],"receiver":[177],"operating":[178],"characteristic":[179],"curves":[180],"(AUROC)":[181],"0.50,":[183],"0.86,":[184],"0.79,":[185],"0.92,":[186],"0.75":[188],"LR,":[190],"RFC,":[191],"C-SVC,":[192],"nu-SVC,":[193,240],"ANN":[196],"respectively.":[197],"nu-SVC":[199],"best,":[201],"with":[202],"highest":[204],"AUROC":[205],"sensitivity":[208],"specificity":[210],"78.8%":[212],"94.9%.":[214],"CONCLUSION:":[215],"Machine":[216,235],"methodologies":[218],"show":[219],"promise":[220],"characterizing":[222],"full":[225],"term":[226],"births":[227],"comorbidity":[229],"information":[230],"administrative":[232],"health":[233],"data.":[234],"techniques,":[237],"such":[238],"as":[239,244],"might":[241],"be":[242],"considered":[243],"supplement":[246],"assessment":[249],"risk.":[252]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-09-27T08:07:27.782135","created_date":"2019-05-16T00:00:00"}
