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Routine administrative data from two hospitals in Northern Ireland are used to compare algorithms and support resource planning and risk-aware performance management.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/paper-templates/","Paper Templates",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/using-data-mining-to-predict-hospital-admissions-from-the-emergency-department/177694/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/using-data-mining-to-predict-hospital-admissions-from-the-emergency-department/177694.png","ImageObject",442,249,{"name":42,"@type":43},"Asher","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/vnd.openxmlformats-officedocument.wordprocessingml.document","2026-10-07","2026-09-02",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"为什么需要用数据挖掘来预测急诊入院？","Question",{"text":63,"@type":64},"急诊拥挤会导致等待时间增加、救护车改道、人员士气下降，并带来不良结局。通过预测入院风险，可用于改进患者流程与资源规划，从而缓解拥挤。","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"本研究使用了哪些算法来构建预测模型？",{"text":68,"@type":64},"使用三种机器学习算法：逻辑回归、决策树以及梯度提升机器（GBM）。",{"name":70,"@type":61,"acceptedAnswer":71},"结果显示哪种模型表现更好？",{"text":72,"@type":64},"GBM 的效果最好，准确率约为80.31%，AUC-ROC为0.859；决策树与逻辑回归的整体准确率略低。","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},177694,1790240146,{"code":4,"msg":82,"data":83},"success",[84,89,94,99,104,109,114,119,123],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":86,"show_sort_weight":87,"slug":88},11,"Presentations",90,"presentations",{"id":90,"doc_module":22,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},12,"Resumes",80,"resumes",{"id":95,"doc_module":22,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},14,"Invoices",70,"invoices",{"id":100,"doc_module":22,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},15,"Posters",60,"posters",{"id":105,"doc_module":22,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},16,"Social Media",50,"social-media",{"id":110,"doc_module":22,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},17,"Forms",40,"forms",{"id":115,"doc_module":22,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},18,"Letters",30,"letters",{"id":120,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":121,"slug":122},21,5,"papers-templates",{"id":124,"doc_module":22,"doc_module_name":25,"category_name":125,"show_sort_weight":4,"slug":126},158,"General","general-158",{"code":4,"msg":82,"data":128},{"doc_id":79,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":22,"category_id":120,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":136,"language":137,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":12,"update_tm":141,"read_time":85},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Using Data Mining to Predict Hospital Admissions\nFrom the Emergency Department\nByron Grahama\u0002*, Raymond Bondb, Michael Quinna and Maurice Mulvennab\na Northern Health and Social Care Trust, Antrim, BT41 2RL\nb School of Computing and Mathematics, Ulster University, Newtownabbey, BT37 0QB\nAbstract\nBackground and Objectives\nCrowding within Emergency Departments (EDs) can have significant negative consequences for patients. EDs therefore need to explore the use of innovative methods to improve patient flow and prevent overcrowding. Therefore, it is crucial to explore innovative methods that could be used to improve patient flow to help prevent ED crowding. One potential method is the use of data mining using machine learning techniques to predict ED admissions, which can be incorporated into hospital dashboards or clinical systems, thus  providing a decision support tool to facilitate performance management and planning of the patient journey. facilitating improved planning of the patient journey.\nMethods\nThis study uses routinely collected administrative data (120,600 records) from two major acute hospitals in Northern Ireland to compare contrasting machine learning algorithms in predicting the risk of an ED attender being admitted to hospital. Three algorithms are used to build the predictive models: (1) logistic regression, (2) decision trees, and (3) gradient boosted machines (GBM).\nResults\nThe GBM performed better (accuracy=80.31%, AUC-ROC=0.859) than theAll three models had similar predictive ability, with little difference in overall accuracy between the gradient boosted machine (accuracy=80.31%, AUC-ROC=0.859), decision tree (accuracy=80.06%, AUC-ROC=0.824) and the logistic regression model (accuracy=79.94%, AUC-ROC=0.849). Drawing on logistic regression, sSeveral variables were identified using the multivariate logistic regression model as being associated with admissions including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year.\nConclusions\nThis study highlights the potential utility of three common machine learning algorithms in predicting patient admissions. Practical implementation of the models developed in this study would provide a snapshot of likely predicted admissions from the emergency department at a given time, allowing for advance resource planning and the avoidance bottlenecks in patient flow, as well as comparison of predicted and actual admission rates. In scenarios where interpretability is a key consideration, EDs should consider adopting models based on logistic regression, although slightly higher levels of accuracy can be achieved using GBM’s, making the case for GBM in scenarios where accuracy is paramount. As the three models have similar levels of accuracy, consideration should be given to the complexity and interpretability of the models for practical use, which would lead to a strong case for EDs to adopt the more interpretable logistic regression model.\nKeywords: Data Mining; Machine Learning; Hospital Admissions; Emergency Department; Crowding\n1 Introduction\nEmergency department (ED) crowding can have serious negative consequences for patients and staff, such as increased wait time, ambulance diversion, reduced staff morale, adverse patient outcomes such as increased mortality, and cancellation of elective procedures \u0013ADDIN CSL_CITATION { \"citationItems\" : [ { \"id\" : \"ITEM-1\", \"itemData\" : { \"DOI\" : \"10.1016/j.jemermed.2005.05.023\", \"ISBN\" : \"0736-4679\", \"ISSN\" : \"07364679\", \"PMID\" : \"16677993\", \"abstract\" : \"Emergency Department (ED) crowding and ambulance diversion have been increasingly significant national problems for more than a decade. Surveys of hospital directors have reported overcrowding in almost every state and 91% of hospital ED directors report overcrowding as a problem. The problem has developed because of multiple factors in the past 20 years, including a steady downsizing in hospital c","cbCaifhnzXgzQIHh","https://ap.wps.com/l/cbCaifhnzXgzQIHh","docx",196677,31,"English","# Abstract\n## Background and Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"为什么需要用数据挖掘来预测急诊入院？\",\"answer\":\"急诊拥挤会导致等待时间增加、救护车改道、人员士气下降，并带来不良结局。通过预测入院风险，可用于改进患者流程与资源规划，从而缓解拥挤。\"},{\"question\":\"本研究使用了哪些算法来构建预测模型？\",\"answer\":\"使用三种机器学习算法：逻辑回归、决策树以及梯度提升机器（GBM）。\"},{\"question\":\"结果显示哪种模型表现更好？\",\"answer\":\"GBM 的效果最好，准确率约为80.31%，AUC-ROC为0.859；决策树与逻辑回归的整体准确率略低。\"}]","Using Data Mining to Predict Hospital Admissions - From the Emergency Department | DOCX",1788328873]