[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290145-105":59,"doc-detail-290145-en":130},{"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":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","prognosis-prediction-in-traumatic-brain-injury-patients-using-machine-learning-algorithms","Prognosis prediction in traumatic brain injury patients using machine learning algorithms","","Predicting treatment outcomes in traumatic brain injury (TBI) remains challenging. This study builds accurate machine learning (ML) models to forecast TBI outcomes using demographic features, laboratory data, imaging indices, and clinical variables. Data from 3347 Iranian tertiary trauma patients (2016–2021) were used, with 1653 remaining after exclusions. Random forest and decision tree with ten-fold cross-validation were evaluated.",{"@graph":69,"@context":122},[70,84,105],{"@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/prognosis-prediction-in-traumatic-brain-injury-patients-using-machine-learning-algorithms/290145/",{"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/prognosis-prediction-in-traumatic-brain-injury-patients-using-machine-learning-algorithms/290145.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the goal of the study on TBI patients?","Question",{"text":112,"@type":113},"To develop accurate machine learning algorithms that predict treatment outcomes for traumatic brain injury patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which dataset size and time range were used?",{"text":117,"@type":113},"The study used data from 3347 patients admitted from 2016 to 2021, and 1653 patients remained after excluding incomplete data.",{"name":119,"@type":110,"acceptedAnswer":120},"Which clinical features were most reliable for predicting in-hospital mortality and long-term survival?",{"text":121,"@type":113},"The motor component of the Glasgow coma scale, pupil condition, and cistern condition were most reliable for in-hospital mortality, while age was the key feature for long-term survival.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290145,1789640798,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},1099513958607,"https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPrognosis prediction in traumatic brain injury patients using machine learning algorithms  \nHosseinali Khalili1, Maziyar Rismani2, Mohammad Ali Nematollahi 3,  \nMohammad Sadegh Masoudi1, Arefeh Asadollahi4, Reza Taheri1*, Hossein Pourmontaseri2,5, Adib Valibeygi2, Mohamad Roshanzamir6, RoohallahAlizadehsani7, Amin Niakan1,  \nArefAndishgar2, Sheikh Mohammed Shariful Islam8,9,10 & U. Rajendra Acharya11,12,13  \nPredicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes ofTBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients’ age takes the place of cisterns condition when considering the long-term survival ofTBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality ofTBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy  \nrate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients’survival in the short-and long-term.  \nTraumatic brain injury (TBI) is among the most common causes of in-hospital death and neurological disabilities1. Recent observations showed that the mortality and morbidity of TBI are growing2,3. Over the last two decades, several studies have been dedicated to investigating the risk factors related to TBI morbidity and mortality. For instance, it has been found that age, gender, and the severity ofTBI play essential roles in 10-year mortality4. Further investigations also introduced multiple risk factors for TBI mortality, such as intracranial pressure (ICP), using alcohol, the intensity of care, oxidative stress imbalance, and grouping complications. Although different risk factors have been distinguished in recent years, we still have a long way to go to achieve accurate assessment scales to manage patients with TBI5–7.  \nGlasgow coma scale (GCS) is a popular tool to assess the neurological condition of patients with different brain injuries, especially TBI. Although GCS provides a reliable measurement for clinicians to manage the TBI, more efficient predictors are required to predict the outcomes of these cases8. Glasgow outcome scale (GOS) was another tool recruited to monitor the long-term recovery of patients, which has been extended from 5 to 8 classes (extended GOS or GOSE) to provide a more detailed follow-up9. It appropriately depicts the clinical  \n1Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran. 2Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran. 3Department of Computer Sciences, Fasa University, Fasa, Iran. 4Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran. 5Bitab Knowledge Enterprise, Fasa University of Medical Sciences, Fasa, Iran. 6Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa 74617-81189, Iran. 7Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia. 8Institute for Physical Activit","cbCaitXymsHwL8VT","https://ap.wps.com/l/cbCaitXymsHwL8VT","pdf",1571259,15,"English","# Introduction\n## Rationale for prognosis prediction in TBI\n## Tools: GCS and outcome scales\n## Role of machine learning\n## Literature review","[{\"question\":\"What was the goal of the study on TBI patients?\",\"answer\":\"To develop accurate machine learning algorithms that predict treatment outcomes for traumatic brain injury patients.\"},{\"question\":\"Which dataset size and time range were used?\",\"answer\":\"The study used data from 3347 patients admitted from 2016 to 2021, and 1653 patients remained after excluding incomplete data.\"},{\"question\":\"Which clinical features were most reliable for predicting in-hospital mortality and long-term survival?\",\"answer\":\"The motor component of the Glasgow coma scale, pupil condition, and cistern condition were most reliable for in-hospital mortality, while age was the key feature for long-term survival.\"}]","Prognosis prediction in traumatic brain injury patients using machine learning algorithms | PDF",38]