[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124053-en":3,"doc-seo-124053-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124053,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Artificial Intelligence and Machine Learning in Trauma Outcome Prediction - A Literature Review","Trauma remains a leading cause of mortality and disability worldwide, while reliable outcome prediction is still difficult despite medical advances. Artificial intelligence and machine learning can identify patterns and analyze complex relationships in trauma patient data to support clinical decision-making. This literature review uses PRISMA to evaluate studies from major databases, extracting design, population, interventions, outcomes, and methodological quality. Results include 15 included studies, where common algorithms are XGBoost and neural networks.","099. Artificial Intelligence and Machine Learning in Trauma Outcome Prediction: A Literature Review  \nIvan Hisar Marolop Sihombing2, Sonar Soni Panigoro1  \n1Oncology Surgery Division, Department of Surgery, dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia  \n2Department of Surgery, dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia  \nIntroduction: Trauma remains a leading cause of mortality and disability globally, particularly among young adults. Despite advancements, accurate prediction of trauma outcomes remains challenging. Artificial intelligence (AI) and machine learning (ML) has the potential to detect patterns and analyzing complex relationship of data from trauma patients to support decision making in the management of trauma. This study aims to explore the potential of AI and ML to enhance trauma outcome prediction in order to healthcare workers with basic understanding of AI and ML and the potential for them to utilize it management of trauma patients. Methods: A literature review was conducted using studies from PubMed, SCOPUS, Cochrane, EBSCOHost, and ScienceDirect database using PRISMA protocol. Data extraction focused on study design, population, interventions, outcomes, and methodological quality using the Oxford Centre for Evidence-Based Medicine levels of evidence. Results: From the literature review, 15 studies from total of 585 studies were included in this literature review. XGBoost and Neural Networks were the most common algorithms (60%) . While six studies predicted mortality, others focused on length of stay and discharge outcomes. Overall, ML significantly improved trauma diagnosis by enhancing predictive models for injury severity and mortality. Conclusions: Machine learning models have shown superior performance compared to traditional scoring systems in their ability to predict trauma outcomes and could effectively forecast critical outcomes and guide treatment decisions. Subsequent studies should concentrate on creating more resilient models based on prospectively gathered data to enhance the performance of created algorithms.  \nKeywords: trauma, artificial intelligence, machine learning, outcome prediction  \nDOI: [https://doi.org/10.24843/JBN.2024.v08.is02.p099](https://doi.org/10.24843/JBN.2024.v08.is02.p099)  \nABSTRACT","cbCaiev5EzubQiXT","https://ap.wps.com/l/cbCaiev5EzubQiXT","pdf",74620,1,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"What is the purpose of using AI and ML in trauma outcome prediction?\",\"answer\":\"AI and ML aim to detect patterns and analyze complex trauma patient data to improve support for clinical decision-making and prediction of outcomes.\"},{\"question\":\"How was the literature review conducted?\",\"answer\":\"The review followed PRISMA and searched PubMed, SCOPUS, Cochrane, EBSCOHost, and ScienceDirect, extracting study design, population, interventions, outcomes, and evidence quality.\"},{\"question\":\"What algorithms and outcomes were most commonly reported?\",\"answer\":\"XGBoost and neural networks were the most common algorithms. Some studies predicted mortality, while others focused on length of stay and discharge-related outcomes.\"}]","Artificial Intelligence and Machine Learning in Trauma Outcome Prediction - A Literature Review | PDF",1785820117,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"artificial-intelligence-and-machine-learning-in-trauma-outcome-prediction-a-literature-review","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/artificial-intelligence-and-machine-learning-in-trauma-outcome-prediction-a-literature-review/124053/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What is the purpose of using AI and ML in trauma outcome prediction?","Question",{"text":73,"@type":74},"AI and ML aim to detect patterns and analyze complex trauma patient data to improve support for clinical decision-making and prediction of outcomes.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How was the literature review conducted?",{"text":78,"@type":74},"The review followed PRISMA and searched PubMed, SCOPUS, Cochrane, EBSCOHost, and ScienceDirect, extracting study design, population, interventions, outcomes, and evidence quality.",{"name":80,"@type":71,"acceptedAnswer":81},"What algorithms and outcomes were most commonly reported?",{"text":82,"@type":74},"XGBoost and neural networks were the most common algorithms. 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