[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126235-en":3,"doc-seo-126235-105":31,"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":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},126235,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Predicting Health Care Facility Stay Duration - A Machine Learning Approach","The COVID-19 pandemic exposed weaknesses in healthcare management, especially around bed occupancy and resource allocation during surges. The work targets inpatient Length of Stay (LoS) prediction to improve efficiency, reduce infection risk, lower mortality, and decrease occupied beds. A predictive model based on Random Forest Regression is built using a 2010 inpatient dataset from the New York Department of Health, including preprocessing, encoding, missing-value handling, and evaluation with multiple database variants and scaling methods. Results show a mean absolute error of 2.93 on the normal database, indicating stronger-than-standard accuracy.","Predicting Health Care Facility Stay Duration: A Machine Learning Approach  \nGeorgios Kleitou  \nDepartment of Science and Engineering Southampton Solent University Southampton, United Kingdom [kleitougeorgioswork@gmail.com](kleitougeorgioswork@gmail.com)  \nJarutas Andritsch  \nDepartment of Science and Engineering Southampton Solent University Southampton, United Kingdom [jarutas.andritsch@solent.ac.uk](jarutas.andritsch@solent.ac.uk)  \nAbstract—The COVID 19 pandemic revealed shortcomings in healthcare, particularly concerning bed occupancy and resource allocation. During the Delta variant wave, it was highlighted how much improvement is needed in management strategies. One promising solution is the prediction of inpatient Length of Stay. Accurate predictions can enhance efficiency, reduce infection risks, lower mortality rates and decrease bed occupancy. This research proposes a predictive model using Random Forest Regression to accurately forecast hospital length of stay, aiming to enhance resource management and patient care. We utilized a 2010 inpatient dataset from the New York Department of Health and conducted thorough data preprocessing, including cleaning, handling missing values, and numerical encoding of categorical variables for regression. Additionally, we experimented with three database variations: one with targeted and frequency encoding, another using synthetic minority oversampling technique for handling imbalances, and a third applying synthetic minority oversampling technique for regression with gaussian noise for continuous variables. Each database was tested with and without scaling using four different scalers. The objective was to achieve a mean absolute error below the industry standard of 6.5, prioritizing unbiased metrics. Our results indicate that the final model achieved a 2.93 mean absolute error on the normal database, demonstrating its effectiveness in predicting length of stay. The study underlined the potential of machine learning inaccurately predicting the Length of Stay in hospitals and the possibility of a more accurate model of the industry standard. Further advancements could be made to the models with more balanced datasets and a user-friendly interface for hospital staff usage.  \nKeywords— length of stay, machine learning, health care, Random Forest, Regression  \nI. INTRODUCTION  \nThe COVID-19 pandemic indicated various shortcomings in healthcare on a global scale. Reports of overcrowding, mishandling of space and resource allocation resulted in a lack of oxygen and beds [1] . When the Delta variant followed, the situation escalated affecting over 78 countries [2] . In an effort to examine the situation the Cyber Security and Infrastructure Agency developed a predictive model based on the data they possessed which was 75% of bed occupancy resulted in 12000 excess deaths, then the prediction showcased that in the duration of 2 weeks that could spiral in a 100% bed occupancy and 80000 excess deaths [3] . Researchers have discovered that prolonged length of state (PLoS) had tremendous impact on bed occupancy. The studies made revealed that targeting extended length of stay (LoS) can optimize a health care facility’s resource distribution [4] . Thus, tackling LoS is essential to handling healthcare facility efficiency. LoS represents the duration spent by an inpatient in a healthcare  \nfacility [5] . Successful supervision of LoS can improve resource distribution, decrease infection risks, lower mortality rates, declined bed occupancy and grow the profits of the facility [6] . The National Health Service (NHS) has been investigating hospital stay since 2018 aiming to improve on leadership, communication, support and research [7] . This experiment was done to uncover prediction of an inpatients LoS with precision based on various variables of the patient and regression models.  \nII. LITERATURE REVIEW  \nA. Models Used in prior research  \nA noteworthy study in Saudi Arabia aimed to predict the","cbCaiuIHrf1xJOzB","https://ap.wps.com/l/cbCaiuIHrf1xJOzB","pdf",223163,6,1,4,"English","en",105,"# Introduction\n# Literature Review\n## Models Used in prior research\n## Feature Importance","[{\"question\":\"Why is predicting inpatient Length of Stay (LoS) important?\",\"answer\":\"Accurate LoS predictions can improve resource distribution, reduce infection risks, lower mortality rates, and decrease bed occupancy, supporting better healthcare efficiency.\"},{\"question\":\"What modeling approach is proposed for LoS prediction?\",\"answer\":\"The research uses Random Forest Regression and tests multiple database variations, including targeted/frequency encoding and imbalance handling with synthetic oversampling.\"},{\"question\":\"What performance did the final model achieve?\",\"answer\":\"The final model reached a mean absolute error of 2.93 on the normal database, demonstrating strong effectiveness in forecasting length of stay.\"}]","Predicting Health Care Facility Stay Duration - A Machine Learning Approach | PDF",1785903974,10,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"predicting-health-care-facility-stay-duration-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@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/research-report/",3,{"item":53,"name":13,"@type":44,"position":22},"https://docshare.wps.com/document/predicting-health-care-facility-stay-duration-a-machine-learning-approach/126235/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",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},"Why is predicting inpatient Length of Stay (LoS) important?","Question",{"text":76,"@type":77},"Accurate LoS predictions can improve resource distribution, reduce infection risks, lower mortality rates, and decrease bed occupancy, supporting better healthcare efficiency.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What modeling approach is proposed for LoS prediction?",{"text":81,"@type":77},"The research uses Random Forest Regression and tests multiple database variations, including targeted/frequency encoding and imbalance handling with synthetic oversampling.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance did the final model achieve?",{"text":85,"@type":77},"The final model reached a mean absolute error of 2.93 on the normal database, demonstrating strong effectiveness in forecasting length of stay.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":30,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":30,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]