[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128461-en":3,"doc-seo-128461-105":31,"detail-sidebar-cat-0-en-105":85},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128461,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit","A prospective observational cohort study develops and validates machine learning models that predict medication errors in neonatal intensive care unit (NICU) patients by integrating both pharmacotherapy and work-environment variables. The system targets medication error types across prescribing, preparation, administration, and monitoring, using patient-related parameters and care-provider workload features. Analysis of medication orders and prescriptions reports high predictive performance for the presence of medication errors, supporting precision screening and potential targeted implementation to reduce preventable NICU medication harm.","EUR Research Information Portal  \nDevelopment and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit  \nPublished in:  \nFrontiers in Pharmacology  \nPublication status and date:  \nPublished: 14/04/2023  \nDOI (link to publisher):  \n10.3389/fphar.2023.1151560  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY  \nCitation for the published version (APA):  \nYalçın, N. , Kaşıkcı, M. , Çelik, H. T. , Allegaert, K. , Demirkan, K. , Yiğit, Ş . , & Yurdakök, M. (2023) . Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit. Frontiers in Pharmacology, 14, Article 1151560. [https://doi.org/10.3389/fphar.2023.1151560](https://doi.org/10.3389/fphar.2023.1151560)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nTYPE Original Research PUBLISHED 14 April 2023  \nDOI 10.3389/fphar.2023.1151560  \nOPEN ACCESS  \nEDITED BY  \nCatherine M. T. Sherwin,  \nWright State University, United States  \nREVIEWED BY  \nOmiya Hassan,  \nUniversity of Missouri, United States Matitiahu Berkovitch,  \nYitzhak Shamir Medical Center, Israel  \n*CORRESPONDENCE  \nNadir Yalçın,  \n [nadir.yalcin@hacettepe.edu.tr](nadir.yalcin@hacettepe.edu.tr)  \nRECEIVED 26 January 2023  \nACCEPTED 04 April 2023  \nPUBLISHED 14 April 2023  \nCITATION  \nYalçın N, Kaşıkcı M, Çelik HT, Allegaert K, Demirkan K, Yiğit Ş and Yurdakök M (2023), Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.  \nFront. Pharmacol. 14:1151560 .  \ndoi: 10.3389/fphar.2023.1151560  \nCOPYRIGHT  \n© 2023 Yalçın, Kaşıkcı, Çelik, Allegaert, Demirkan, Yiğit and Yurdakök. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDevelopment and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit  \nNadir Yalç ın 􀀁 1*, Merve Kaşıkcı 􀀁 2, Hasan Tolga Çelik 􀀁 3, Karel Allegaert 􀀁 4,5,6, Kutay Demirkan 􀀁 1, Şule Yiğit 􀀁 3 and Murat Yurdakök 􀀁 3  \n1Department of Clinical Pharmacy, Faculty of Pharmacy, Hacettepe University, Ankara, Türkiye, 2Department of Biostatistics, Faculty of Medicine, Hacettepe University, Ankara, Türkiye, 3Division of Neon","cbCaif0M0PRhkZUw","https://ap.wps.com/l/cbCaif0M0PRhkZUw","pdf",1046570,5,1,9,"English","en",105,"# Study overview\n## Aim and design\n## Setting and participants\n## Results and model performance\n## Conclusion and implications","[{\"question\":\"What factors were most strongly correlated with medication error occurrence?\",\"answer\":\"The model used patient-related parameters such as total drug count and drug classes, along with work-environment parameters such as weekly working hours of physicians and nurses and nurses’ monthly shifts.\"}]","Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit | PDF",1786001191,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":80,"head_meta":82,"extra_data":84,"updated_unix":29},"development-and-validation-of-a-machine-learning-based-detection-system-to-improve-precision-screening-for-medication-errors-in-the-neonatal-intensive-care-unit","",{"@graph":37,"@context":79},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/development-and-validation-of-a-machine-learning-based-detection-system-to-improve-precision-screening-for-medication-errors-in-the-neonatal-intensive-care-unit/128461/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-30","2026-08-06",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73],{"name":74,"@type":75,"acceptedAnswer":76},"What factors were most strongly correlated with medication error occurrence?","Question",{"text":77,"@type":78},"The model used patient-related parameters such as total drug count and drug classes, along with work-environment parameters such as weekly working hours of physicians and nurses and nurses’ monthly shifts.","Answer","https://schema.org",{"og:url":53,"og:type":81,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":83,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":86},[87,91,95,99,103,108,111,116,120,123,127],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":88,"show_sort_weight":89,"slug":90},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":92,"show_sort_weight":93,"slug":94},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":106,"slug":107},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":109,"slug":110},40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},8,"Research & Report",30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":20,"slug":130},19,"General","general"]