[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121020-en":3,"doc-seo-121020-105":30,"detail-sidebar-cat-0-en-105":95},{"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},121020,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Early prediction of ventilator-associated pneumonia with machine learning models - A systematic review and meta-analysis of prediction model performance","Machine learning prediction models are used to structure, classify, and relate multimodal data to support clinical decisions across diagnosis, prognosis, and therapy. Early prediction of ventilator-associated pneumonia (VAP) is expected to speed diagnosis and inform preventive actions for adults undergoing invasive mechanical ventilation. This systematic review and meta-analysis, conducted with Cochrane methods, evaluates predictive performance, interpretability, technological readiness, and risk of bias.","European Journal of Internal Medicine 121 (2024) 76–87  \nContents lists available at ScienceDirect  \nEuropean Journal of Internal Medicine  \njournal [homepage: www.elsevier.com/locate/ejim](homepage: www.elsevier.com/locate/ejim)  \n| Original Article\u003Cbr>Early prediction of ventilator-associated pneumonia with machine learning models: A systematic review and meta-analysis of prediction\u003Cbr>model performance✰\u003Cbr>Tuomas Frondeliusa, Irina Atkovab, Jouko Miettunenc, d, Jordi Relloe, f, g, Gillian Vesty h, Han Shi Jocelyn Chewi, Miia Jansson a,j, *\u003Cbr>a Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland b University of Oulu, Oulu, Finland\u003Cbr>c Research Unit of Population Health, University of Oulu, Oulu, Finland\u003Cbr>d Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland e Global Health eCore, Vall d’Hebron Institute of Research (VHIR), Barcelona, Spain\u003Cbr>f Centro de Investigacion Biomedica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain\u003Cbr>g Unit´e de Recherche FOVERA, R´eanimation Douleur Urgences, Centre Hospitalier Universitaire de Nîmes, Nîmes, France h School of Accounting, RMIT University, Melbourne, Australia\u003Cbr>i Alice Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore j Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland, RMIT University, Melbourne, Australia |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Artificial intelligence Artificial ventilation Machine learning Meta-analysis Predictive analytics\u003Cbr>Ventilator-associated pneumonia |  | Background: Machine learning-based prediction models can catalog, classify, and correlate large amounts of multimodal data to aid clinicians at diagnostic, prognostic, and therapeutic levels. Early prediction of ventilatorassociated pneumonia (VAP) may accelerate the diagnosis and guide preventive interventions. The performance of a variety of machine learning-based prediction models were analyzed among adults undergoing invasive mechanical ventilation.\u003Cbr>Methods: This systematic review and meta-analysis was conducted in accordance with the Cochrane Collaboration. Machine learning-based prediction models were identified from a search of nine multi-disciplinary databases. Two authors independently selected and extracted data using predefined criteria and data extraction forms. The predictive performance, the interpretability, the technological readiness level, and the risk of bias of the included studies were evaluated.\u003Cbr>Results: Final analysis included 10 static prediction models using supervised learning. The pooled area under the receiver operating characteristics curve, sensitivity, and specificity for VAP were 0.88 (95 % CI 0.82–0.94, I2 98.4 %), 0.72 (95 % CI 0.45–0.98, I2 97.4 %) and 0.90 (95 % CI 0.85–0.94, I2 97.9 %), respectively. All included studies had either a high or unclear risk of bias without significant improvements in applicability. The carerelated risk factors for the best performing models were the duration of mechanical ventilation, the length of ICU stay, blood transfusion, nutrition strategy, and the presence of antibiotics.\u003Cbr>Conclusion: A variety of the prediction models, prediction intervals, and prediction windows were identified to facilitate timely diagnosis. In addition, care-related risk factors susceptible for preventive interventions were identified. In future, there is a need for dynamic machine learning models using time-depended predictors in conjunction with feature importance of the models to predict real-time risk of VAP and related outcomes to optimize bundled care. |  |\n\nRegistration: PROSPERO CRD42022367014, registered on 24-10-2022.  \n* Corresponding author.  \n[E-mail address:](E-mail address: miia.jansson@oulu.fi)[ miia.jansson@oulu.fi](E-mail address: miia.jansson@oulu.fi) (M. Jansson)","cbCaia20jNpUdNhr","https://ap.wps.com/l/cbCaia20jNpUdNhr","pdf",1331589,1,12,"English","en",105,"# Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the main goal of the systematic review and meta-analysis?\",\"answer\":\"To analyze the predictive performance and related qualities of machine learning-based models for early prediction of ventilator-associated pneumonia (VAP) in adults receiving invasive mechanical ventilation.\"},{\"question\":\"How was the literature for the study selected and evaluated?\",\"answer\":\"Models were identified by searching nine multidisciplinary databases, then two authors independently selected and extracted data using predefined criteria and forms. Predictive performance, interpretability, technological readiness level, and risk of bias were assessed.\"},{\"question\":\"What predictive performance metrics were reported for the best aggregated models?\",\"answer\":\"For VAP, pooled AUC, sensitivity, and specificity were reported as 0.88, 0.72, and 0.90, respectively, alongside high heterogeneity (I² values above 97%).\"},{\"question\":\"Which patient or care-related risk factors were linked to the best performing models?\",\"answer\":\"Care-related risk factors included duration of mechanical ventilation, length of ICU stay, blood transfusion, nutrition strategy, and the presence of antibiotics.\"}]","Early prediction of ventilator-associated pneumonia with machine learning models - A systematic review and meta-analysis of prediction model performance | PDF",1785733344,30,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"early-prediction-of-ventilator-associated-pneumonia-with-machine-learning-models-a-systematic-review-and-meta-analysis-of-prediction-model-performance","",{"@graph":36,"@context":89},[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/early-prediction-of-ventilator-associated-pneumonia-with-machine-learning-models-a-systematic-review-and-meta-analysis-of-prediction-model-performance/121020/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the systematic review and meta-analysis?","Question",{"text":75,"@type":76},"To analyze the predictive performance and related qualities of machine learning-based models for early prediction of ventilator-associated pneumonia (VAP) in adults receiving invasive mechanical ventilation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the literature for the study selected and evaluated?",{"text":80,"@type":76},"Models were identified by searching nine multidisciplinary databases, then two authors independently selected and extracted data using predefined criteria and forms. Predictive performance, interpretability, technological readiness level, and risk of bias were assessed.",{"name":82,"@type":73,"acceptedAnswer":83},"What predictive performance metrics were reported for the best aggregated models?",{"text":84,"@type":76},"For VAP, pooled AUC, sensitivity, and specificity were reported as 0.88, 0.72, and 0.90, respectively, alongside high heterogeneity (I² values above 97%).",{"name":86,"@type":73,"acceptedAnswer":87},"Which patient or care-related risk factors were linked to the best performing models?",{"text":88,"@type":76},"Care-related risk factors included duration of mechanical ventilation, length of ICU stay, blood transfusion, nutrition strategy, and the presence of antibiotics.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":125},"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]