[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122460-en":3,"doc-seo-122460-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122460,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review - Review","Early identification of pharmacological therapy for gestational diabetes mellitus (GDM) could improve therapeutic strategy and treatment efficiency, but current evidence varies in model design and readiness. This scoping review systematically summarised machine learning models used to predict the need for pharmacological therapy in GDM, following PRISMA-ScR guidance and quality appraisal with PROBAST. Searches covered Embase, Medline, IEEE Xplore and Web of Science from 1 July 2007 to 31 August 2024.","Received: 31 January 2025 | Accepted: 29 October 2025  \nDOI: 10. 1111/dme.70171  \nREVIEW  \nThe use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review  \nJasmine R. Kirkwood1  | Natasha Galloway2 | Robert S. Lindsay3  | Areti Manataki4  | Deborah J. Wake5  | Rebecca M. Reynolds1   \n1Centre for Cardiovascular Science, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK  \n2Edinburgh Centre for Endocrinology and Diabetes, NHS Lothian Hospitals Trust, Edinburgh, UK  \n3School of Cardiovascular and Metabolic Health, The University of Glasgow, Glasgow, UK  \n4School of Computer Science, University of St Andrews, St Andrews, UK  \n5Usher Institute, The University of Edinburgh, Edinburgh, UK  \nCorrespondence  \nJasmine R. Kirkwood and Rebecca M. Reynolds, Centre for Cardiovascular Science, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh EH16 4TJ, UK. [Email: j.r.kirkwood@sms.ed.ac.uk and](Email: j.r.kirkwood@sms.ed.ac.uk and)[r.reynolds@ed.ac.uk](r.reynolds@ed.ac.uk)  \nFunding information  \nMedical Research Scotland  \nAbstract  \nAims: Early identification of pharmacological therapy for gestational diabetes mellitus (GDM), a common pregnancy complication, through machine learning could allow for better therapeutic strategies and improved treatment efficiency. This scoping review aimed to comprehensively review the machine learning models used to predict the need for pharmacological therapy in GDM.  \nMethods: Four electronic databases—Embase, Medline, IEEE Xplore and Web of Science—were searched for publications between 1 July 2007 and 31 August 2024. Studies predicting pharmacological therapy for GDM using machine learning were included. The Joanna Briggs Institute and PRISMA-ScR checklist was followed, and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to assess quality.  \nResults: Included were 17 studies presenting 44 models, 61.4%(27/44) predicted any pharmacological therapy use and 38.6%(17/44) predicted insulin use alone. All were binary classifiers, and logistic regression was typically used. The overall area under the receiver operating curve had a median of 0.75. Common clinical variables were found to be predictors, such as history of GDM, gestational week at GDM diagnosis, pregestational body mass index, maternal age, HbA1c, fasting and 1h glucose from 75 g oral glucose tolerance test. Though 65.9% of models were validated, there was a lack of external validation. There was no evidence of clinical application of the models.  \nConclusion: Logistic regression with common clinical variables was often used to predict pharmacological therapy for GDM. Few models were externally validated or clinically applicable.  \nKEYWORDS  \ngestational diabetes mellitus (GDM), insulin, machine learning, oral agents, pharmacological therapy, prediction algorithms  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s). Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.  \nDiabetic Medicine. 2025;00:e70171 .  \n[https://doi.org/10.1111/dme.70171](https://doi.org/10.1111/dme.70171)  \n[wileyonlinelibrary.com/journal/dme](wileyonlinelibrary.com/journal/dme)  \n1 of 11  \n2 of 11  \nKIRKWOOD et al.  \n1 | INTRODUCTION  \nGestational diabetes mellitus (GDM) is one of the most common pregnancy complications, affecting 13.4% of live births worldwide in 20191 and is rising in prevalence.2 Women with GDM typically attend multi-disciplinary specialised clinics fortnightly, where their self-monitoring blood glucose values (SMBG) and diet and exercise modifications are reviewed.3,4 If blood glucose targets are unmet, some women will need medication, such as oral agents and/or insulin injections.4  \nGDM increases the risk of adverse pregnancy and neonatal ou","cbCaiajDIXiKhb6r","https://ap.wps.com/l/cbCaiajDIXiKhb6r","pdf",1017562,1,11,"English","en",105,"# Introduction\n## Rationale for early identification\n## Role of machine learning\n# Methods\n## Search strategy and inclusion criteria\n## Quality assessment\n# Results\n## Model types and performance\n## Predictors used\n## Validation and clinical application\n# Conclusion","[{\"question\":\"What was the aim of this scoping review?\",\"answer\":\"To comprehensively review machine learning models used to predict the need for pharmacological therapy in gestational diabetes mellitus (GDM), including their methods, variables, and quality.\"},{\"question\":\"Which databases and time period were searched?\",\"answer\":\"Embase, Medline, IEEE Xplore, and Web of Science were searched for publications from 1 July 2007 to 31 August 2024.\"},{\"question\":\"What did the review find about model performance and validation?\",\"answer\":\"Most models were binary classifiers with logistic regression commonly used, and the median area under the receiver operating curve was 0.75; although many models were validated, external validation and clinical application were limited.\"}]","The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review - Review | PDF",1785810767,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-use-of-machine-learning-to-predict-pharmacological-therapy-in-gestational-diabetes-a-scoping-review-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-use-of-machine-learning-to-predict-pharmacological-therapy-in-gestational-diabetes-a-scoping-review-review/122460/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the aim of this scoping review?","Question",{"text":75,"@type":76},"To comprehensively review machine learning models used to predict the need for pharmacological therapy in gestational diabetes mellitus (GDM), including their methods, variables, and quality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which databases and time period were searched?",{"text":80,"@type":76},"Embase, Medline, IEEE Xplore, and Web of Science were searched for publications from 1 July 2007 to 31 August 2024.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the review find about model performance and validation?",{"text":84,"@type":76},"Most models were binary classifiers with logistic regression commonly used, and the median area under the receiver operating curve was 0.75; 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