[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128659-en":3,"doc-seo-128659-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},128659,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic - evidence from Wuhan-area hospitals","The COVID-19 pandemic has exerted unprecedented pressure on healthcare systems because patient length of stay (LOS) is highly variable and difficult to forecast. This study uses electronic medical record data from Zhongnan Hospital of Wuhan University and applies six machine learning algorithms to model LOS probability for patients before and during the pandemic. After variable selection, 35 factors are identified, with out-of-pocket amount, medical insurance, and admission deplanement ranking highest. Results show XGBoost delivers the best accuracy and supports resource planning for hospital administrators.","TYPE Original Research PUBLISHED 13 December 2024 DOI 10.3389/fdgth.2024.1506071  \nEDITED BY  \nHosna Salmani,  \nIran University of Medical Sciences, Iran  \nREVIEWED BY  \nMukaila Rahman,  \nLagos State University, Nigeria Raphael Oladeji Akangbe, Lagos State Government, Nigeria Kayalvizhi Jayavel,  \nUniversity of the Arts London, United Kingdom *CORRESPONDENCE  \nYang Liu  \n [yang.liu27@whu.edu.cn](yang.liu27@whu.edu.cn)  \nRECEIVED 04 October 2024  \nACCEPTED 03 December 2024  \nPUBLISHED 13 December 2024  \nCITATION  \nLiu Y, Liang R and Zhang C (2024) The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals.  \nFront. Digit. Health 6:1506071 .  \ndoi: 10.3389/fdgth.2024.1506071  \nCOPYRIGHT  \n© 2024 Liu, Liang and Zhang. This is an openaccess 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.  \nThe application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals  \nYang Liu1,2*, Renzhao Liang3 and Chengzhi Zhang4  \n1School of Information Management, Wuhan University, Wuhan, China, 2Shenzhen Research Institute, Wuhan University, Shenzhen, China, 3School of Physics and Technology, Wuhan University, Wuhan, China, 4 Department of Information Management, Nanjing University of Science & Technology, Nanjing, China  \nObjective: The COVID-19 pandemic has placed unprecedented strain on healthcare systems, mainly due to the highly variable and challenging to predict patient length of stay (LOS) . This study aims to identify the primary factors impacting LOS for patients before and during the COVID-19 pandemic. Methods: This study collected electronic medical record data from Zhongnan Hospital of Wuhan University. We employed six machine learning algorithms to predict the probability of LOS.  \nResults: After implementing variable selection, we identified 35 variables affecting the LOS for COVID-19 patients to establish the model. The top three predictive factors were out-of-pocket amount, medical insurance, and admission deplanement. The experiments conducted showed that XGBoost (XGB) achieved the best performance. The MAE, RMSE, and MAPE errors before and during the COVID-19 pandemic are lower than 3% on average for household registration in Wuhan and non-household registration in Wuhan. Conclusions: Research finds machine learning is reasonable in predicting LOS before and during the COVID-19 pandemic. This study offers valuable guidance to hospital administrators for planning resource allocation strategies that can effectively meet the demand. Consequently, these insights contribute to improved quality of care and wiser utilization of scarce resources.  \nKEYWORDS  \nlength of stay, COVID-19 pandemic, machine leaning, medical insurance, household registration  \n1 Introduction  \nThe rapid global spread of the coronavirus disease (COVID-19) since 2019 has posed asignificant threat to healthcare systems worldwide (1) . One of the key challenges resulting from the surge in infections is the increased demand for hospital beds (2) . However, hospital beds are limited; if the demand for beds exceeds hospital capacity, this will severely reduce the quality of care provided (3) . For example, during the early stages of the outbreak, many people died in Wuhan due to infection because they were not admitted to hospital. Therefore, accurately predicting the demand for hospital beds is crucial to proactively expand capacity and indicate the effectiveness of public health  \nFrontiers in Digital Health 01 [frontiersin.","cbCaieVI62ps73zD","https://ap.wps.com/l/cbCaieVI62ps73zD","pdf",13099562,1,15,"English","en",105,"# Introduction\n## Study objective\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To identify key factors affecting patient length of stay (LOS) before and during the COVID-19 pandemic and build predictive models using machine learning.\"},{\"question\":\"How was LOS prediction performed in this research?\",\"answer\":\"The study collected electronic medical record data from Zhongnan Hospital of Wuhan University and applied six machine learning algorithms to predict LOS probability.\"},{\"question\":\"Which machine learning model performed best, and what were the top predictive factors?\",\"answer\":\"XGBoost achieved the best performance. The top three predictive factors were out-of-pocket amount, medical insurance, and admission deplanement.\"}]","The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic - evidence from Wuhan-area hospitals | PDF",1786002370,38,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"the-application-of-machine-learning-algorithms-for-predicting-length-of-stay-before-and-during-the-covid-19-pandemic-evidence-from-wuhan-area-hospitals","",{"@graph":36,"@context":86},[37,54,69],{"@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/the-application-of-machine-learning-algorithms-for-predicting-length-of-stay-before-and-during-the-covid-19-pandemic-evidence-from-wuhan-area-hospitals/128659/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of this study?","Question",{"text":76,"@type":77},"To identify key factors affecting patient length of stay (LOS) before and during the COVID-19 pandemic and build predictive models using machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was LOS prediction performed in this research?",{"text":81,"@type":77},"The study collected electronic medical record data from Zhongnan Hospital of Wuhan University and applied six machine learning algorithms to predict LOS probability.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed best, and what were the top predictive factors?",{"text":85,"@type":77},"XGBoost achieved the best performance. 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