[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127850-en":3,"doc-seo-127850-105":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},127850,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting future hospital antimicrobial resistance prevalence using machine learning","Antimicrobial resistance (AMR) poses a major global health threat, and forecasting it at an aggregated hospital level can better target interventions. The study uses machine learning to combine historical AMR prevalence and antimicrobial usage data from England’s hospitals across 22 pathogen–antibiotic combinations. Extreme Gradient Boosting (XGBoost) models are evaluated against prior-value forecasting and linear trend forecasting, with feature importance for interpretability. Results show XGBoost provides the strongest predictions.","| \u003Cbr>[https://doi.org/10.1038/s43856-024-00606-8](https://doi.org/10.1038/s43856-024-00606-8) |  |  |\n| --- | --- | --- |\n| Predicting future hospital antimicrobial resistance prevalence using machine learning\u003Cbr> Check for updates |  |  |\n| Karina-Doris Vihta 1,2,3 , Emma Pritchard1,2, Koen B. Pouwels 2,4, Susan Hopkins5, Rebecca L. Guy 5, Katherine Henderson 5, Dimple Chudasama5, Russell Hope 5, Berit Muller-Pebody5, Ann Sarah Walker1,2,6,10, David Clifton 3,7,10 & David W. Eyre  1,2,6,8,9,10 |  |  |\n| Abstract\u003Cbr>Background Predicting antimicrobial resistance (AMR), a top global health threat, nationwide at an aggregate hospital level could help target interventions. Using machine learning, we exploit historical AMR and antimicrobial usage to predict future AMR. Methods Antimicrobial use and AMR prevalence in bloodstream infections in hospitals in England were obtained per hospital group (Trust) and financial year (FY, April–March) for 22 pathogen–antibiotic combinations (FY2016-2017 to FY2021-2022) . Extreme Gradient Boosting(XGBoost)model predictions were comparedtothe previous valuetaken forwards, the difference between the previous two years taken forwards and linear trend forecasting (LTF). XGBoost feature importances were calculated to aid interpretability.\u003Cbr>Results Here we show that XGBoost models achieve the best predictive performance. Relatively limited year-to-year variability in AMR prevalence within Trust–pathogen–antibiotic combinations means previous value taken forwards also achieves a low mean absolute error (MAE), similar to or slightly higher than XGBoost. Using the difference between the previous two years taken forward or LTF performs consistently worse. XGBoost considerably outperforms all other methods in Trusts with a larger change inAMR prevalence from FY2020-2021(last training year)to FY2021-2022(held-out test set) . Feature importance values indicate that besides historical resistance to the same pathogen–antibiotic combination as the outcome, complex relationships between resistance in different pathogens to the same antibiotic/antibiotic class and usage are exploited for predictions. These are generally among the top ten features ranked according to their mean absolute SHAP values.\u003Cbr>Conclusions Year-to-year resistance has generally changed little within Trust–pathogen–antibiotic combinations. In those with larger changes, XGBoost models can improve predictions, enabling informed decisions, efficient resource allocation, and targeted interventions. |  | Plain language summary\u003Cbr>Antibiotics play an important role in treating serious bacterial infections. However, withthe increased usage of antibiotics, they are becoming less effective. In our study, we use machine learning to learn from past antibiotic resistance and usage in order to predict what resistance will look like in the future. Different hospitals across England have very different resistance levels, however, within each hospital, these levels remain stable over time. When larger changes in resistance occurred over time in individual hospitals, our methods were able to predict these. Understanding how much resistance there is in hospital populations, and what may occur in the future can help determine where resources and interventions should be directed. |\n| Antimicrobial resistance is oneofthe top global health threats1. Bloodstream infections are typically oneofthe most serious types ofinfection;given their high mortality/morbidity, they are generally treated in hospitals and therefore are often used for surveillance of resistance. In high-income countries, any isolated pathogens will be tested for antimicrobial susceptibility against key antibiotics, while, unfortunately, most low and middle- | income countries lack the laboratory capacity to test all bloodstream pathogens, if any2. Being able to predict future antimicrobial resistance of bloodstream infections in networks of hospitals could help target interventions and allocate ","cbCaikBnDR4iXzgl","https://ap.wps.com/l/cbCaikBnDR4iXzgl","pdf",3236296,2,1,14,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Plain language summary\n# Introduction","[{\"question\":\"What data and time range were used to predict future AMR prevalence?\",\"answer\":\"The study uses antimicrobial usage and AMR prevalence in bloodstream infections in hospitals in England, aggregated by hospital trust and financial year from April–March across FY2016-2017 to FY2021-2022 for 22 pathogen–antibiotic combinations.\"},{\"question\":\"How does the XGBoost approach compare with simpler forecasting methods?\",\"answer\":\"XGBoost achieves the best predictive performance overall. Using the previous year’s value taken forward also performs well, while using the difference between the previous two years or linear trend forecasting consistently performs worse.\"},{\"question\":\"What do the feature importance results suggest about drivers of predictions?\",\"answer\":\"Feature importance indicates that beyond prior resistance to the same pathogen–antibiotic combination, the models leverage complex relationships among resistances across different pathogens for the same antibiotic or antibiotic class, along with usage patterns.\"}]","Predicting future hospital antimicrobial resistance prevalence using machine learning | PDF",1785942355,35,{"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-future-hospital-antimicrobial-resistance-prevalence-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/predicting-future-hospital-antimicrobial-resistance-prevalence-using-machine-learning/127850/",4,{"url":52,"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},"What data and time range were used to predict future AMR prevalence?","Question",{"text":76,"@type":77},"The study uses antimicrobial usage and AMR prevalence in bloodstream infections in hospitals in England, aggregated by hospital trust and financial year from April–March across FY2016-2017 to FY2021-2022 for 22 pathogen–antibiotic combinations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the XGBoost approach compare with simpler forecasting methods?",{"text":81,"@type":77},"XGBoost achieves the best predictive performance overall. Using the previous year’s value taken forward also performs well, while using the difference between the previous two years or linear trend forecasting consistently performs worse.",{"name":83,"@type":74,"acceptedAnswer":84},"What do the feature importance results suggest about drivers of predictions?",{"text":85,"@type":77},"Feature importance indicates that beyond prior resistance to the same pathogen–antibiotic combination, the models leverage complex relationships among resistances across different pathogens for the same antibiotic or antibiotic class, along with usage patterns.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"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":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]