[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127599-en":3,"doc-seo-127599-105":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},127599,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Predicting no-show appointments in a pediatric hospital in Chile using machine learning","Chile’s public health system covers 74% of the population, while missed appointments (no-shows) average 19%, exceeding the national 15% target shared by private care. This study uses a case dataset from Doctor Luis Calvo Mackenna Hospital (a public high-complexity pediatric teaching center in Santiago), where historical no-show rates reached 29%. Machine learning models predict no-shows from demographic, social, and historical variables (2015–2018), addressing class imbalance and applying cost-effective thresholding. Results show 20.4% no-shows overall, strong links to prior non-attendance, and an 8-week reminder strategy achieving a 10.3-point reduction.","University of Groningen  \nPredicting no-show appointments in a pediatric hospital in Chile using machine learning  \nDunstan, J. ; Villena, F. ; Hoyos, J. P. ; Riquelme, V. ; Royer, M. ; Ramírez, H. ; Peypouquet, J.  \nPublished in:  \nHealth Care Management Science  \nDOI:  \n10.1007/s10729-022-09626-z  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nDunstan, J. , Villena, F. , Hoyos, J. P. , Riquelme, V. , Royer, M. , Ramírez, H. , & Peypouquet, J. (2023) .  \nPredicting no-show appointments in a pediatric hospital in Chile using machine learning. Health Care Management Science, 26, 313–329 . [https://doi.org/10.1007/s10729-022-09626-z](https://doi.org/10.1007/s10729-022-09626-z)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 01-01-2026  \nPredicting no-show appointments in a pediatric hospital in Chile using machine learning  \nJ. Dunstan1,2  · F. Villena1  · J. P. Hoyos3  · V. Riquelme1  · M. Royer4 · H. Ram1,5  · J. Peypouquet6   \nReceived: 8 August 2021 / Accepted: 13 December 2022 / Published online: 28 January 2023 © The Author(s) 2023  \nAbstract  \nThe Chilean public health system serves 74% of the country’s population, and 19% of medical appointments are missed on average because of no-shows. The national goal is 15%, which coincides with the average no-show rate reported in the private healthcare system. Our case study, Doctor Luis Calvo Mackenna Hospital, is a public high-complexity pediatric hospital and teaching center in Santiago, Chile. Historically, it has had high no-show rates, up to 29% in certain medical specialties. Using machine learning algorithms to predict no-shows of pediatric patients in terms of demographic, social, and historical variables. To propose and evaluate metrics to assess these models, accounting for the cost-effective impact of possible intervention strategies to reduce no-shows. We analyze the relationship between a no-show and demographic, social, and historical variables, between 2015 and 2018, through the following traditional machine learning algorithms: Random Forest, Logistic Regression, Support Vector Machines, AdaBoost and algorithms to alleviate the problem of class imbalance, such as RUS Boost, Balanced Random Forest, Balanced Bagging and Easy Ensemble. These class imbalances arise from the relatively low number of no-shows to the total number of appointments. Instead of the default thresholds used by each method, we computed alternative ones via the minimization of a weighted average of type I ","cbCaib9SP261t71n","https://ap.wps.com/l/cbCaib9SP261t71n","pdf",2967324,1,18,"English","en",105,"# Abstract\n# Introduction\n# Methodology: Machine learning models and class imbalance handling\n## Feature/variable analysis: demographic, social, and historical factors\n## Cost-effective thresholding and evaluation metrics\n# Results and discussion: prediction performance and key risk factors\n## Experimental design and reminder intervention impact\n# Highlights\n## Daily/specialty ranked appointment lists","[{\"question\":\"What problem does the study target in Chilean pediatric care?\",\"answer\":\"It targets high rates of missed medical appointments (no-shows) in a public pediatric hospital, where overall and specialty-specific no-show rates are substantial and sometimes reach around 29%.\"},{\"question\":\"Which data and variables are used to predict no-shows?\",\"answer\":\"Predictions use demographic, social, and historical variables derived from appointment records between 2015 and 2018.\"},{\"question\":\"How do the models address class imbalance and evaluation costs?\",\"answer\":\"The work uses imbalance-handling algorithms (e.g., RUS Boost, Balanced Random Forest, Balanced Bagging, Easy Ensemble) and computes alternative decision thresholds by minimizing a weighted average of type I and II errors based on cost-effectiveness criteria.\"},{\"question\":\"What intervention strategy does the study test, and what effect does it have?\",\"answer\":\"An 8-week experimental design tests a reminder strategy for higher-risk patients, resulting in a 10.3 percentage-point reduction in no-shows compared with a control group.\"}]","Predicting no-show appointments in a pediatric hospital in Chile using machine learning | 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problem does the study target in Chilean pediatric care?","Question",{"text":75,"@type":76},"It targets high rates of missed medical appointments (no-shows) in a public pediatric hospital, where overall and specialty-specific no-show rates are substantial and sometimes reach around 29%.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data and variables are used to predict no-shows?",{"text":80,"@type":76},"Predictions use demographic, social, and historical variables derived from appointment records between 2015 and 2018.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the models address class imbalance and evaluation costs?",{"text":84,"@type":76},"The work uses imbalance-handling algorithms (e.g., RUS Boost, Balanced Random Forest, Balanced Bagging, Easy Ensemble) and computes alternative decision thresholds by minimizing a weighted average of type I and II errors based on cost-effectiveness criteria.",{"name":86,"@type":73,"acceptedAnswer":87},"What intervention strategy does the study test, and what effect does it have?",{"text":88,"@type":76},"An 8-week experimental design tests a reminder strategy for higher-risk patients, resulting in a 10.3 percentage-point reduction in no-shows compared with a control group.","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 & 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