[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116900-en":3,"doc-seo-116900-105":30,"detail-sidebar-cat-0-en-105":92},{"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},116900,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine learning applications on neonatal sepsis treatment: a scoping review","Neonatal sepsis is a leading cause of health loss and mortality worldwide, and its nonspecific signs make diagnosis difficult while treatment remains labor-intensive and costly. Antimicrobial resistance further worsens outcomes, with many neonatal bloodstream infections resistant to first-line antibiotics. This scoping review synthesizes evidence on how machine learning supports clinicians in diagnosing neonatal sepsis and informing empiric antibiotic selection. Eighteen studies are assessed, detailing key predictors and commonly used model types.","O’Sullivan et al. BMC Infectious Diseases (2023) 23:441 BMC Infectious Diseases  \n[https://doi.org/10.1186/s12879-023-08409-3](https://doi.org/10.1186/s12879-023-08409-3)  \nRESEARCH Open Access  \nMachine learning applications on neonatal sepsis treatment: a scoping review  \nColleen O’Sullivan1*, Daniel Hsiang-TeTsai2,3, Ian Chang-Yen Wu2,3,4, Emanuela Boselli5, Carmel Hughes 1, Deepak Padmanabhan6 andYingfen Hsia 1,2  \nAbstract  \nIntroduction Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are nonspecific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment.  \nMethods PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning.  \nResults There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks.  \nConclusion Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.  \nKeywords Neonate, Bloodstream Infection, Machine Learning, Antibiotic, Antimicrobial Resistance  \n*Correspondence: Colleen O’Sullivan [cosullivan07@qub.ac.uk](cosullivan07@qub.ac.uk)  \n1School of Pharmacy, Queen’s University Belfast, Belfast, UK  \n2Centre for Neonatal and Paediatric Infection, St. George’s, University of London, London, UK  \n3School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan  \n4Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan 5Department of Pediatrics, V. Buzzi Children’s Hospital, University of Milan, Milan, Italy  \n6School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, UK  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/. The](http://creativecommons.org/licenses/by","cbCaiokSWLvfe6KA","https://ap.wps.com/l/cbCaiokSWLvfe6KA","pdf",2262829,1,10,"English","en",105,"# Introduction\n## Neonatal sepsis burden and diagnostic challenges\n## Antimicrobial resistance and treatment implications\n# Methods\n## Literature search strategy\n# Results\n## Included studies and model focus\n## Key predictors and best-performing models\n# Conclusion","[{\"question\":\"What is the purpose of this scoping review?\",\"answer\":\"The review presents applications of machine learning on neonatal sepsis treatment, focusing on diagnosis support and selection of appropriate empiric antibiotics.\"},{\"question\":\"How many studies were included, and what were their main focuses?\",\"answer\":\"Eighteen studies were included. Three addressed machine learning for antibiotic treatment in bloodstream infections, one predicted in-hospital mortality, and the rest developed prediction models for diagnosing possible sepsis.\"},{\"question\":\"Which predictors and model types showed strong performance?\",\"answer\":\"Gestational age, C-reactive protein, and white blood cell count were important for diagnosing sepsis. Age, weight, and time from hospital admission to blood sampling supported prediction of antibiotic-resistant infections. Random forest and neural networks performed best.\"}]","Machine learning applications on neonatal sepsis treatment: a scoping review | PDF",1785672361,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-applications-on-neonatal-sepsis-treatment-a-scoping-review","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-applications-on-neonatal-sepsis-treatment-a-scoping-review/116900/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the purpose of this scoping review?","Question",{"text":76,"@type":77},"The review presents applications of machine learning on neonatal sepsis treatment, focusing on diagnosis support and selection of appropriate empiric antibiotics.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How many studies were included, and what were their main focuses?",{"text":81,"@type":77},"Eighteen studies were included. Three addressed machine learning for antibiotic treatment in bloodstream infections, one predicted in-hospital mortality, and the rest developed prediction models for diagnosing possible sepsis.",{"name":83,"@type":74,"acceptedAnswer":84},"Which predictors and model types showed strong performance?",{"text":85,"@type":77},"Gestational age, C-reactive protein, and white blood cell count were important for diagnosing sepsis. Age, weight, and time from hospital admission to blood sampling supported prediction of antibiotic-resistant infections. Random forest and neural networks performed best.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]