{"id":"https://openalex.org/W4407832002","doi":"https://doi.org/10.1109/icaiqsa64000.2024.10882273","title":"Prediction Model for Preterm Birth Using Deep Learning","display_name":"Prediction Model for Preterm Birth Using Deep Learning","publication_year":2024,"publication_date":"2024-12-20","ids":{"openalex":"https://openalex.org/W4407832002","doi":"https://doi.org/10.1109/icaiqsa64000.2024.10882273"},"language":"en","primary_location":{"id":"doi:10.1109/icaiqsa64000.2024.10882273","doi":"https://doi.org/10.1109/icaiqsa64000.2024.10882273","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiqsa64000.2024.10882273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5116372688","display_name":"Abhiraj Rajesh Bhoi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhiraj Rajesh Bhoi","raw_affiliation_strings":["G H Raisoni College of Engineering,Departemnt of Computer Science and Engineering,Nagpur,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering,Departemnt of Computer Science and Engineering,Nagpur,India","institution_ids":["https://openalex.org/I4210146358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089289725","display_name":"Girish R. Talmale","orcid":"https://orcid.org/0000-0002-0180-5228","observed_orcids":["https://orcid.org/0000-0002-0180-5228"]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Girish Talmale","raw_affiliation_strings":["G H Raisoni College of Engineering,Departemnt of Computer Science and Engineering,Nagpur,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering,Departemnt of Computer Science and Engineering,Nagpur,India","institution_ids":["https://openalex.org/I4210146358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063588747","display_name":"Prashant Khobragade","orcid":"https://orcid.org/0000-0002-9646-0604","observed_orcids":["https://orcid.org/0000-0002-9646-0604"]},"institutions":[{"id":"https://openalex.org/I4210146358","display_name":"Raisoni Group of Institutions","ror":"https://ror.org/03dp11s58","country_code":"IN","type":"education","lineage":["https://openalex.org/I4210146358"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prashant Khobragade","raw_affiliation_strings":["G H Raisoni College of Engineering,Departemnt of Computer Science and Engineering,Nagpur,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"G H Raisoni College of Engineering,Departemnt of Computer Science and Engineering,Nagpur,India","institution_ids":["https://openalex.org/I4210146358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210146358"],"apc_list":null,"apc_paid":null,"fwci":3.8671,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.94725937,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8241999745368958,"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":0.8241999745368958,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.10869999974966049,"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/T11290","display_name":"Preterm Birth and Chorioamnionitis","score":0.027799999341368675,"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/preterm-birth-prediction","display_name":"preterm birth prediction","score":0.9879999756813049},{"id":"https://openalex.org/keywords/deep-learning","display_name":"deep learning","score":0.8309999704360962},{"id":"https://openalex.org/keywords/preterm-labour","display_name":"preterm labour","score":0.5910000205039978},{"id":"https://openalex.org/keywords/risk-stratification","display_name":"risk stratification","score":0.8100000023841858},{"id":"https://openalex.org/keywords/maternal-health-indicators","display_name":"maternal health indicators","score":0.8659999966621399},{"id":"https://openalex.org/keywords/early-warning-model","display_name":"early warning model","score":0.5529999732971191},{"id":"https://openalex.org/keywords/transvaginal-ultrasound","display_name":"transvaginal ultrasound","score":0.8489999771118164},{"id":"https://openalex.org/keywords/electronic-health-records","display_name":"electronic health records","score":0.9760000109672546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6382695436477661},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.512290358543396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45947378873825073},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42777860164642334},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.06958076357841492}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icaiqsa64000.2024.10882273","doi":"https://doi.org/10.1109/icaiqsa64000.2024.10882273","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icaiqsa64000.2024.10882273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.9476000070571899,"id":"https://metadata.un.org/sdg/3"}],"sustainable_development_goals_aurora":[{"display_name":"Gender equality","score":0.550000011920929,"id":"https://metadata.un.org/sdg/5"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1525197404","https://openalex.org/W1578780401","https://openalex.org/W1975755498","https://openalex.org/W1992370145","https://openalex.org/W2112927272","https://openalex.org/W2148044501","https://openalex.org/W2166266395","https://openalex.org/W2218383638","https://openalex.org/W2323523193","https://openalex.org/W2326159517","https://openalex.org/W2487776182","https://openalex.org/W2765390878","https://openalex.org/W2783906820","https://openalex.org/W2786814109","https://openalex.org/W2801294580","https://openalex.org/W2984738305","https://openalex.org/W2990504763","https://openalex.org/W2996887806","https://openalex.org/W3123425497","https://openalex.org/W3166998164","https://openalex.org/W3167873632","https://openalex.org/W3177144647","https://openalex.org/W3184267879","https://openalex.org/W3217753766","https://openalex.org/W4214646484","https://openalex.org/W4224026226","https://openalex.org/W4225512838","https://openalex.org/W4243054876","https://openalex.org/W4317178680","https://openalex.org/W4317178713","https://openalex.org/W4393062840","https://openalex.org/W4400491112","https://openalex.org/W4401359549","https://openalex.org/W4401359647","https://openalex.org/W4401360000","https://openalex.org/W4404418885"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"The":[0,14],"pregnancy":[1],"women":[2],"reaches":[3],"to":[4,56,89,139,160,207],"40":[5],"weeks":[6,43],"which":[7,186,223],"is":[8,28,34,47,54,96,112,131,166],"the":[9,20,57,117,140,153,192],"average":[10],"for":[11,155,199],"gestation":[12],"period.":[13],"only":[15,126],"issue":[16,98],"will":[17,187,216],"sometimes":[18],"be":[19,225],"possibility":[21],"of":[22,37,44,59,92,119,136,146,194],"preterm":[23,162],"birth":[24,36,53,210],"but":[25],"otherwise":[26],"it":[27],"not":[29],"a":[30,38,65,113,134,156],"major":[31],"thing.":[32],"This":[33,111],"because":[35],"neonate":[39],"delivered":[40],"before":[41],"37":[42],"gestational":[45],"period":[46],"termed":[48],"as":[49,72],"premature":[50,209],"birth.":[51,163],"Preterm":[52,129],"harmful":[55],"health":[58,69,102,201],"both":[60],"an":[61,97,167,182],"infant":[62],"child":[63],"and":[64,76,85,105,144,204,221],"mother.":[66],"Babies":[67],"have":[68,180],"problems":[70],"such":[71],"physical":[73,101],"conditions,":[74],"brain":[75],"development":[77],"issues,":[78],"delays":[79],"continued":[80],"over":[81],"years":[82],"in":[83,122,177,211,218],"growth":[84],"weight":[86],"gain,":[87],"vulnerability":[88],"long-term":[90],"effects":[91],"chronic":[93],"diseases.":[94],"Motherhood":[95],"that":[99,170],"has":[100],"implications,":[103],"emotional":[104],"psychological":[106],"impacts,":[107],"family":[108],"dynamics":[109],"impact.":[110],"global":[114],"problem.":[115],"With":[116],"deficiency":[118],"healthcare":[120],"facilities":[121],"rural":[123],"India,":[124],"things":[125,174],"get":[127],"worse.":[128],"labour":[130],"caused":[132],"by":[133],"combination":[135],"factors":[137,230],"relating":[138],"mother,":[141],"medical":[142],"conditions":[143],"type":[145],"medication.":[147],"In":[148],"this":[149,176],"paper,":[150],"we":[151],"outline":[152],"approaches":[154],"deep":[157],"learning":[158,165],"model":[159,185],"predict":[161,208],"Deep":[164],"emerging":[168],"technology":[169],"can":[171],"help":[172,217],"with":[173,197],"like":[175],"healthcare.":[178],"We":[179],"present":[181],"early-warning":[183],"prediction":[184,222],"inform":[188],"being":[189],"analyzed":[190],"from":[191],"lens":[193],"immaturity":[195],"risk":[196,219],"solutions":[198],"maternal":[200],"sensitive":[202],"indicators":[203],"associated":[205],"patterns":[206],"expected":[212],"schools.":[213],"These":[214],"findings":[215],"stratification":[220],"may":[224],"based":[226],"on":[227],"various":[228],"predictive":[229],"(as":[231],"EHR,":[232],"EHG,":[233],"Trans-vaginal":[234],"U/S":[235],"etc).":[236]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}
