[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-160273-en":3,"doc-seo-160273-105":30,"detail-sidebar-cat-0-en-105":95},{"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":4,"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},160273,1374402968488,"Đào","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning in Nursing - A Cross-Disciplinary Review","Rapid advancement of artificial intelligence (AI) and machine learning (ML) is transforming healthcare and inevitably affects nursing practice, yet limited formal education can leave many nurses hesitant to use these tools in everyday care. This cross-disciplinary review explains core ML concepts, key algorithm types, and common evaluation metrics, then surveys major nursing research applications and their ethical and practical clinical challenges. A structured search (2015-2024) identified 61 eligible studies from 1445 records, highlighting supervised dominance, typical evaluation measures, key application categories, and adoption barriers. It proposes three guiding pillars—interdisciplinary collaboration, systematic integration in nursing education, and a framework linking human-centered interventions to interpretable ML tools—to support safe, bias-aware, ethically responsible technology-enhanced care.","Review began 05/11/2025  \nReview ended 07/01/2025  \nPublished 07/02/2025  \n© Copyright 2025  \nKosmidis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CCBY 4.0. , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited.  \nDOI: 10.7759/cureus.87181  \nOpen Access Review Article  \nMachine Learning in Nursing: A CrossDisciplinary Review  \nDimitrios Kosmidis 1 , Dimitrios Simopoulos 2 , Konstantinos Anastasopoulos 3, Sotiria Koutsouki 4  \n1. Department of Nursing, Democritus University of Thrace, Alexandroupolis, GRC 2. Department of Medicine, Democritus University of Thrace, Alexandroupolis, GRC 3. Electrical and Computer Engineering, University of Patras, Patras, GRC 4. Intensive Care Unit, General Hospital of Kavala, Kavala, GRC  \nCorresponding author: Dimitrios Kosmidis, [dkosmidis@nurs.duth.gr](dkosmidis@nurs.duth.gr)  \nAbstract  \nRapid advancement of artificial intelligence (AI) and machine learning (ML) is transforming healthcare, and nursing practice is inevitably affected. Yet limited formal education leaves many nurses hesitant to integrate such tools into everyday care.  \nThis cross‑disciplinary review (i) introduces the fundamental concepts, core types and common evaluation metrices, illustrated with nursing-specific examples; (ii) catalogues the main algorithmic applications in nursing research ; and (iii) describes ethical and practical challenges in their clinical use.  \nA structured search of PubMed, Embase, Scopus, IEEE Xplore and ACM Digital Library (2015-2024) retrieved 1445 records; after de-duplication and screening against inclusion criteria (peer-reviewed, English-language studies that developed, implemented or evaluated an ML method in a clinical, community-health or educational nursing context), 61 papers were analyzed. Supervised approaches predominated, while unsupervised and semi-supervised techniques were less common. Models were evaluated mainly with accuracy, area under the receiver operating characteristic curve (AUROC), precision-recall and F 1-score for classification, or mean-absolute/squared error for regression.  \nApplications spanned eight main categories: (1) predictive risk assessment and early‑warning systems; (2) clinical decision support and diagnostic aid; (3) continuous patient monitoring; (4) workflow, staffing and operational optimizations; (5) documentation and information extraction via natural‑language processing;  \n(6) education and competency development; and (7) other niche applications.  \nKey barriers to wider adoption remain regulatory and ethical constraints, data quality, model transparency and engagement issues, data challenges, lack of ML-specific training in nursing curricula, and operational limitations. The utilization of ML in nursing is based on three core pillars: strong interdisciplinary collaboration, systematic integration of ML at all levels of nursing education, and a guiding framework that maps human-centered nursing interventions to interpretable ML tools. Such a foundation will enable nurses to safely leverage the results of algorithms, avoid biases and risks, and integrate ethical responsibility into technology-enhanced care.  \nCategories: Other, Medical Education, Healthcare Technology  \nKeywords: artificial intelligence, clinical decision support, machine learning, nursing, nursing informatics, predictive analytics  \nIntroduction And Background  \nInterdisciplinary collaboration between nursing and informatics can significantly impact patient care [1-3].  \nNursing science contributes to extensive clinical experience, established theories, and ethical frameworks, rooted in human interaction-elements challenging to analyze or interpret mathematically [4] . Identifying complex patterns to provide personalized health predictions requires collecting and processing large volumes of heterogeneous clinical data. The technical la","cbCaicMQli7pw7Sl","https://ap.wps.com/l/cbCaicMQli7pw7Sl","pdf",1093189,1,15,"English","en",105,"# Abstract\n## Search strategy and study selection\n## Evaluation metrics and methodological trends\n## Main ML application categories in nursing\n## Adoption barriers and ethical challenges\n## Core pillars for safe clinical integration","[{\"question\":\"What is the main purpose of this review on ML in nursing?\",\"answer\":\"The review introduces fundamental ML concepts and evaluation metrics, maps major algorithmic applications in nursing research, and outlines ethical and practical challenges for clinical use.\"},{\"question\":\"How many studies were included and what search sources were used?\",\"answer\":\"A structured search of PubMed, Embase, Scopus, IEEE Xplore, and ACM Digital Library (2015-2024) retrieved 1445 records, and 61 peer-reviewed English-language studies were analyzed after de-duplication and screening.\"},{\"question\":\"Which ML evaluation metrics and modeling approaches were most common?\",\"answer\":\"Supervised approaches predominated. Models were mainly evaluated using accuracy and AUROC, precision-recall and F1-score for classification, or mean-absolute/squared error for regression.\"},{\"question\":\"What barriers limit wider adoption of ML in nursing?\",\"answer\":\"Key barriers include regulatory and ethical constraints, data quality issues, limited model transparency, engagement challenges, insufficient ML-specific training in nursing curricula, and operational limitations.\"}]","Machine Learning in Nursing - A Cross-Disciplinary Review | PDF",1788053144,38,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"machine-learning-in-nursing-a-cross-disciplinary-review","",{"@graph":36,"@context":89},[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/machine-learning-in-nursing-a-cross-disciplinary-review/160273/",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","2026-08-30",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of this review on ML in nursing?","Question",{"text":75,"@type":76},"The review introduces fundamental ML concepts and evaluation metrics, maps major algorithmic applications in nursing research, and outlines ethical and practical challenges for clinical use.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many studies were included and what search sources were used?",{"text":80,"@type":76},"A structured search of PubMed, Embase, Scopus, IEEE Xplore, and ACM Digital Library (2015-2024) retrieved 1445 records, and 61 peer-reviewed English-language studies were analyzed after de-duplication and screening.",{"name":82,"@type":73,"acceptedAnswer":83},"Which ML evaluation metrics and modeling approaches were most common?",{"text":84,"@type":76},"Supervised approaches predominated. Models were mainly evaluated using accuracy and AUROC, precision-recall and F1-score for classification, or mean-absolute/squared error for regression.",{"name":86,"@type":73,"acceptedAnswer":87},"What barriers limit wider adoption of ML in nursing?",{"text":88,"@type":76},"Key barriers include regulatory and ethical constraints, data quality issues, limited model transparency, engagement challenges, insufficient ML-specific training in nursing curricula, and operational limitations.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]