[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290547-105":59,"doc-detail-290547-en":135},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":128,"head_meta":130,"extra_data":132,"updated_unix":134},105,"en","prediction-of-stroke-associated-hospital-acquired-pneumonia-machine-learning-approach","Prediction of stroke-associated hospital-acquired pneumonia - Machine learning approach","","Stroke-associated hospital-acquired pneumonia (HAP) significantly worsens outcomes after stroke. This study evaluates machine learning models for early HAP prediction in ischemic and hemorrhagic stroke patients using national registry data and SHapley Additive exPlanations (SHAP) to clarify key drivers. Data cover January 2014 to July 2022 (9,840 patients), with models including XGBoost, Random Forest, SVM, Logistic Regression, and ANN. Results show ANN achieves the best performance (F1 0.86, AUC 0.94) and SHAP highlights severity, admission factors, vitals, demographics, and arrival mode.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/prediction-of-stroke-associated-hospital-acquired-pneumonia-machine-learning-approach/290547/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/prediction-of-stroke-associated-hospital-acquired-pneumonia-machine-learning-approach/290547.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does this study address?","Question",{"text":113,"@type":114},"It targets early prediction of stroke-associated hospital-acquired pneumonia (HAP) and how it affects post-stroke outcomes and mortality.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning models were trained and compared?",{"text":118,"@type":114},"XGBoost, Random Forest, Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Network (ANN) were trained and evaluated.",{"name":120,"@type":111,"acceptedAnswer":121},"Which model performed best and how was performance measured?",{"text":122,"@type":114},"The ANN model performed best, assessed using metrics including F1-score, AUC, accuracy, precision, recall, log loss, and Brier score.",{"name":124,"@type":111,"acceptedAnswer":125},"What factors were identified as key predictors?",{"text":126,"@type":114},"SHAP identified predictors such as stroke severity, admission location, Glasgow Coma score, admission blood pressure values, ethnicity, stroke type, mode of arrival, and age; higher severity and dysphagia increased HAP risk.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},290547,1789642029,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":134,"read_time":148,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Journal of Stroke and Cerebrovascular Diseases 34 (2025) 108200  \nContents lists available at ScienceDirect  \nJournal of Stroke and Cerebrovascular Diseases  \njournal [homepage:](homepage: www.elsevier.com/locate/jstroke)[ www.elsevier.com/locate/jstroke](homepage: www.elsevier.com/locate/jstroke)  \n| Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach\u003Cbr>Ahmad A. Abujaber, RN, MBA, PhDa, Said Yaseen, MBBSb,\u003Cbr>Abdulqadir J. Nashwan, RN, MSc, PhD(c) a,c,* , Naveed Akhtar, MD, PhD d,\u003Cbr>Yahia Imam, MBBS, MD, FRCP Edin, FEBN, MSc. Clinical Neurology, MSc. Stroke Medicine, FESO, FAANe\u003Cbr>a Nursing Department, Hamad Medical Corporation (HMC), Doha, Qatar\u003Cbr>b School of Medicine, Jordan University of Science and Technology, Irbid, Jordan\u003Cbr>c Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar d Neuroradiology Department, Neuroscience Institute, Hamad Medical Corporation (HMC), Doha, Qatar e Neurology Section, Neuroscience Institute, Hamad Medical Corporation (HMC), Doha, Qatar |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Stroke\u003Cbr>Hospital-acquired pneumonia Machine learning Personalized stroke care Stroke outcomes |  | Background: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Additive exPlanations (SHAP) analysis to identify key predictive factors.\u003Cbr>Methods: We collected data from a national stroke registry covering January 2014 to July 2022, including 9,840 patients diagnosed with ischemic and hemorrhagic strokes. Five machine learning models were trained and evaluated: XGBoost, Random Forest, Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Network (ANN). Performance was assessed using accuracy, precision, recall, F1-score, AUC, log loss, and Brierscore. SHAP analysis was conducted to interpret model outputs.\u003Cbr>Results: The ANN model demonstrated superior performance, with an F1-score of 0.86 and an AUC of 0.94. SHAP analysis identified key predictors: stroke severity, admission location, Glasgow Coma score (GCS), systolic and diastolic blood pressure at admission, ethnicity, stroke type, mode of arrival, and age. Patients with higher stroke severity, dysphagia, and those arriving by ambulance were at increased risk for HAP.\u003Cbr>Conclusion: This study enhances our understanding of early predictive factors for HAP in stroke patients and underlines the potential of machine learning to improve clinical decision-making and personalized care. |\n\nBackground  \nStroke remains a critical global health concern, ranking as the second-leading cause of death and the third-leading cause of death and disability combined, with mortality rates rising particularly in developing nations.1 Annually, 13.7 million new stroke cases are reported, leading to approximately 5.5 million deaths.2 The outcome of a stroke is significantly influenced by post-stroke complications, including mobility-related falls, urinary tract infections (UTIs), pneumonia, and psychological conditions.3,4 Among these, infections are prevalent in the acute phase of stroke, with stroke-associated pneumonia affecting 5–44 % of stroke patients and serving as a major independent risk factor for  \npoor outcomes and increased mortality.5–7  \nStroke patients face an elevated risk of hospital-acquired pneumonia (HAP) due to factors such as stroke-induced immunodepression, the severity of the stroke, advanced age, ICU admission, pre-stroke disability, and dysphagia.8–10 HAP, commonly caused by pathogens such as Enterobacteriaceae, Staphylococcus aureus, and Pseudomonas aeruginosa,11 is often linked to aspiration and impaired reflexes following a stroke.12,13 It contributes to poorer functional outcomes, increased mortality within the first-year post-s","cbCaiuSDuXlbdRGY","https://ap.wps.com/l/cbCaiuSDuXlbdRGY","pdf",1718626,"English","# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What problem does this study address?\",\"answer\":\"It targets early prediction of stroke-associated hospital-acquired pneumonia (HAP) and how it affects post-stroke outcomes and mortality.\"},{\"question\":\"Which machine learning models were trained and compared?\",\"answer\":\"XGBoost, Random Forest, Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Network (ANN) were trained and evaluated.\"},{\"question\":\"Which model performed best and how was performance measured?\",\"answer\":\"The ANN model performed best, assessed using metrics including F1-score, AUC, accuracy, precision, recall, log loss, and Brier score.\"},{\"question\":\"What factors were identified as key predictors?\",\"answer\":\"SHAP identified predictors such as stroke severity, admission location, Glasgow Coma score, admission blood pressure values, ethnicity, stroke type, mode of arrival, and age; higher severity and dysphagia increased HAP risk.\"}]","Prediction of stroke-associated hospital-acquired pneumonia - Machine learning approach | PDF",18]