[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122398-en":3,"doc-seo-122398-105":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":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},122398,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Supporting clinical decision making in the emergency department for paediatric patients using machine learning - A scoping review protocol","Machine learning can function as a clinical decision support system to help clinicians make fast, accurate medical decisions in emergency departments. This scoping review protocol aims to identify and summarise existing research on machine learning CDSS tools, with emphasis on models suitable for paediatric patients where gaps in current knowledge persist. The review will follow Arksey and O’Malley’s scoping framework with additional guidelines, selecting studies from the last five years.","Technological University Dublin  \nARROW@TU Dublin  \n\n| Articles | School of Computer Science |\n| --- | --- |\n| 2023\u003Cbr>Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol\u003Cbr>Fiona Leonard\u003Cbr>Technological University Dublin\u003Cbr>Dympna O. Sullivan\u003Cbr>Technological University Dublin\u003Cbr>John Gilligan\u003Cbr>Technological University Dublin See next page for additional authors\u003Cbr>Follow this and additional works at: [https://arrow.tudublin.ie/scschcomart](https://arrow.tudublin.ie/scschcomart) |  |\n\nRecommended Citation  \nLeonard, F., Sullivan, D. O., Gilligan, J., Shea, N. O., & Barrett, M. J. (2023) . Supporting clinical decision making in the emergency department for paediatric patients using machine learning. PLoS ONE, 18(11 November), Article e0294231 . [https://doi.org/10.1371/journal.pone.0294231](https://doi.org/10.1371/journal.pone.0294231)  \nThis Article is brought to you for free and open access by the School of Computer Science at ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, [please contact vera.ki](please contact vera.ki)[lshaw@tudublin.ie](lshaw@tudublin.ie).  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \nAuthors  \nFiona Leonard, Dympna O. Sullivan, John Gilligan, Nicola O. Shea, and Michael J. Barrett  \nThis article is available at ARROW@TU Dublin: [https://arrow.tudublin.ie/scschcomart/253](https://arrow.tudublin.ie/scschcomart/253)  \nPLOS ONE  \nOPEN ACCESS  \nCitation: Leonard F, O’Sullivan D, Gilligan J, O’Shea N, Barrett MJ (2023) Supporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol. PLoS ONE 18(11): e0294231 . [https://doi](https://doi). org/10 .1371/journal.pone.0294231  \nEditor: Gilbert Sterling Octavius, Universitas Pelita Harapan, INDONESIA  \nReceived: January 31, 2023  \nAccepted: October 28, 2023  \nPublished: November 16, 2023  \nCopyright: © 2023 Leonard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.  \nFunding: The support of the Technological University Dublin Scholarship Programme is gratefully acknowledged. The funders had and will not have a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  \nSTUDY PROTOCOL  \nSupporting clinical decision making in the emergency department for paediatric patients using machine learning: A scoping review protocol  \nFiona Leonard1,2 *, Dympna O’Sullivan1, John Gilligan1, Nicola O’Shea3, Michael  \nJ. Barrett4,5  \n1 School of Computer Science, Technological University Dublin, Dublin, Ireland, 2 Digital Health Department, Children’s Health Ireland, Crumlin, Dublin, Ireland, 3 Library and Information Service, Children’s Health Ireland at Crumlin, Dublin, Ireland, 4 Department of Paediatric Emergency Medicine, Children’s Health Ireland at Crumlin, Dublin, Ireland, 5 Women’s and Children’s Health, School of Medicine, University College Dublin, Dublin, Ireland  \n* [fiona.m.leonard@mytudublin.ie](fiona.m.leonard@mytudublin.ie)  \nAbstract  \nIntroduction  \nMachine learning as a clinical decision support system tool has the potential to assist clinicians who must make complex and accurate medical decisions in fast paced environments such as the emergency department. This paper presents a protocol for a scoping review, with the objective of summarising the existing research on machine learning clinical decision support system tools in the emergency department , focusing ","cbCaiks2wHSdVahS","https://ap.wps.com/l/cbCaiks2wHSdVahS","pdf",743842,1,13,"English","en",105,"# Abstract\n## Introduction\n## Materials and methods\n## Discussion","[{\"question\":\"What is the purpose of the scoping review protocol in this document?\",\"answer\":\"To summarise existing research on machine learning clinical decision support system tools in emergency departments, focusing on models appropriate for paediatric patients where a knowledge gap exists.\"},{\"question\":\"Which scoping review framework guides the study?\",\"answer\":\"The protocol follows the scoping study framework of Arksey and O’Malley along with other guidelines.\"},{\"question\":\"What inclusion and exclusion criteria will be applied?\",\"answer\":\"Included studies cover machine learning CDSS tools for any outcome and population (including paediatric/adult/mixed) used in emergency departments, from the last five years. 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