[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117432-en":3,"doc-seo-117432-105":30,"detail-sidebar-cat-0-en-105":91},{"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},117432,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections - Performance Evaluation and Interpretability Analysis","Multidrug-resistant organism (MDRO) infections threaten global health, especially in intensive care units where delayed identification worsens outcomes. Although machine learning can improve infection prediction, most models remain difficult to interpret for clinical decision-making. This retrospective cohort study analyzed 888 ICU patients (2020–2022) and compared six algorithms using discrimination, accuracy, and calibration metrics. A Random Forest model achieved the best overall performance, while SHAP provided global and case-level interpretability to identify modifiable risk factors, supporting more transparent antimicrobial stewardship.","Infection and Drug Resistance downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nInfection and Drug Resistance  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections:  \nPerformance Evaluation and Interpretability Analysis  \nWenting Zhao 1 , 2 , *, Pei Sun 1 , 2 , *, Wei Li 3 , Linping Shang2 ,4  \n1College of Nursing, Changzhi Medical College, Changzhi, Shanxi, People’s Republic of China; 2College of Nursing, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 3Infection Management Department, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China; 4Nursing Department, the First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Linping Shang, College of Nursing, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China, Email [shanglp2002@163.com](shanglp2002@163.com)  \n\n| Background: Multidrug-resistant organism (MDRO) infections pose a significant global health threat, particularly in intensive care units (ICUs), where delayed identification exacerbates clinical outcomes. Although machine learning (ML) holds promise for infection prediction, the opaque nature of complex algorithms impedes clinical adoption. This study evaluated an interpretable machine learning model incorporating SHapley Additive exPlanations (SHAP) to predict MDRO infections in ICU patients.\u003Cbr>Methods: A retrospective cohort study was conducted on 888 ICU patients (2020–2022) from a tertiary hospital in China. Following TRIPOD guidelines, key predictors were identified using Lasso regression from a comprehensive set of clinical variables, including demographics, treatments, and laboratory data. Six machine learning algorithms—Neural Networks, Random Forests, Support Vector Machines, Logistic Regression, Decision Trees, and Gaussian Naive Bayes—were evaluated based on AUC, accuracy, and calibration curves. SHAP analysis provided both global and local interpretability.\u003Cbr>Results: Among 825 eligible cases (375 MDRO infections), the Random Forest model exhibited the highest performance (AUC = 0.83, accuracy = 76.7%) . SHAP analysis identified urinary catheterization, ventilator use, and prolonged antibiotic exposure as key modifiable risk factors. Case-level interpretation via dynamic force plots illustrated individualized risk stratification. Decision curve analysis indicated clinical utility within probability thresholds of 0.44–0.60.\u003Cbr>Conclusion: This study establishes an interpretable prediction framework integrating RF algorithms with SHAP explainability, balancing predictive accuracy with clinical transparency. The model’s dynamic visualization capabilities support individualized risk assessment and evidence-based antimicrobial stewardship. Integration into hospital information systems with real-time dashboards could enhance early intervention strategies.\u003Cbr>Keywords: MDRO, machine learning, prediction, intensive care unit |\n| --- |\n| Introduction\u003Cbr>Multidrug-resistant organisms (MDRO) are microorganisms, primarily bacteria, that have acquired resistance to three or more antimicrobial classes through diverse genetic mechanisms, significantly restricting therapeutic options.1 This classification encompasses both Gram-positive and Gram-negative bacteria. The escalating threat posed by MDRO to public health results in millions of fatalities annually.2 In recognition of this crisis, the World Health Organization identified antimicrobial resistance as one of the top 10 global health threats in 2019.3\u003Cbr>MDRO infections in intensive care unit (ICU) patients are particularly concerning due to their profound impact on treatment efficacy.4 These infections are associated with increased inpatient mortality, higher readmission rates, |\n\nReceived: 8 November 2024  ","cbCaicNbRkutBlOn","https://ap.wps.com/l/cbCaicNbRkutBlOn","pdf",3758576,1,15,"English","en",105,"# Introduction\n# Methods\n## Study Design and Data\n## Predictor Selection and Model Development\n## Model Evaluation and Interpretability\n# Results\n# Conclusion","[{\"question\":\"What problem does the study address regarding MDRO infections in ICUs?\",\"answer\":\"MDRO infections have a major impact on treatment effectiveness in ICU patients, and delayed identification can worsen clinical outcomes. The study targets early prediction while improving interpretability for clinical use.\"},{\"question\":\"How were predictors and machine learning models built in the study?\",\"answer\":\"The study used a retrospective cohort of 888 ICU patients and applied Lasso regression to identify key predictors from demographics, treatments, and laboratory data. Six algorithms were trained and compared using AUC, accuracy, and calibration curves.\"},{\"question\":\"How does the study interpret the prediction model results?\",\"answer\":\"SHAP analysis was used to provide global and local interpretability, highlighting key modifiable risk factors such as urinary catheterization, ventilator use, and prolonged antibiotic exposure. Case-level dynamic force plots support individualized risk stratification.\"}]","Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections - Performance Evaluation and Interpretability Analysis | PDF",1785675859,38,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-prediction-model-for-multidrug-resistant-organisms-infections-performance-evaluation-and-interpretability-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@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/machine-learning-based-prediction-model-for-multidrug-resistant-organisms-infections-performance-evaluation-and-interpretability-analysis/117432/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address regarding MDRO infections in ICUs?","Question",{"text":75,"@type":76},"MDRO infections have a major impact on treatment effectiveness in ICU patients, and delayed identification can worsen clinical outcomes. The study targets early prediction while improving interpretability for clinical use.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were predictors and machine learning models built in the study?",{"text":80,"@type":76},"The study used a retrospective cohort of 888 ICU patients and applied Lasso regression to identify key predictors from demographics, treatments, and laboratory data. Six algorithms were trained and compared using AUC, accuracy, and calibration curves.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study interpret the prediction model results?",{"text":84,"@type":76},"SHAP analysis was used to provide global and local interpretability, highlighting key modifiable risk factors such as urinary catheterization, ventilator use, and prolonged antibiotic exposure. Case-level dynamic force plots support individualized risk stratification.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]