[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125174-en":3,"doc-seo-125174-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"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},125174,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine learning-based prediction of mortality in pediatric trauma patients","This retrospective study develops and validates machine learning models to predict mortality among pediatric trauma patients (18 years and younger) using data extracted from the National Trauma Data Bank. Clinical and physiologic variables available at trauma admission were used as predictors. The dataset was split into a development cohort (70%) for building four ML models and a validation cohort (30%) for testing. Model performance was evaluated using receiver operating characteristic analysis via AUC, with XGBoost achieving the highest accuracy and AUC.","TYPE Original Research PUBLISHED 27 February 2025 DOI 10.3389/fped.2025.1522845  \nEDITED BY  \nAnna Maria Musolino,  \nBambino Gesù Children’s Hospital (IRCCS), Italy  \nREVIEWED BY  \nLorenzo Di Sarno,  \nAgostino Gemelli University Polyclinic (IRCCS), Italy  \nMarina Ramzy Mourid, Alexandria University, Egypt  \n*CORRESPONDENCE  \nAlex Deleon  \n [Alex_Deleon@alumni.baylor.edu](Alex_Deleon@alumni.baylor.edu)  \n†PRESENT ADDRESS  \nAnish Murala,  \nCollege of Arts & Sciences, Texas A&M University, College Station, TX, United States ‡These authors have contributed equally to this work and share first authorship  \nRECEIVED 05 November 2024  \nACCEPTED 08 January 2025  \nPUBLISHED 27 February 2025  \nCITATION  \nDeleon A, Murala A, Decker I, Rajasekaran K and Moreira A (2025) Machine learning-based prediction of mortality in pediatric trauma patients.  \nFront. Pediatr. 13:1522845 .  \ndoi: 10.3389/fped.2025.1522845  \nCOPYRIGHT  \n© 2025 Deleon, Murala, Decker, Rajasekaranand Moreira. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-based prediction of mortality in pediatric trauma patients  \nAlex Deleon1*‡, Anish Murala2†‡, Isabelle Decker1, Karthik Rajasekaran3 and Alvaro Moreira2  \n1Long School of Medicine, UT Health San Antonio, San Antonio, TX, United States, 2Division of Neonatology, Department of Pediatrics, UT Health San Antonio, San Antonio, TX, United States, 3Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA, United States  \nBackground: This study aimed to develop a predictive model for mortality outcomes among pediatric trauma patients using machine learning (ML) algorithms. Methods: We extracted data on a cohort of pediatric trauma patients (18 years and younger) from the National Trauma Data Bank (NTDB) . The main aim was to identify clinical and physiologic variables that could serve as predictors for pediatric trauma mortality. Data was split into a development cohort (70%) to build four ML models and then tested in a validation cohort (30%) . The area under the receiver operating characteristic curve (AUC) was used to assess each model’s performance.  \nResults: In 510,381 children, the gross mortality rate was 1 . 6%(n = 8,250) . Most subjects were male (67%, n = 342,571) and white (62%, n = 315,178) . The AUCs of the four models ranged from 92.7 to 97.7 with XGBoost demonstrating the highest AUC. XGBoost demonstrated the highest accuracy of 97 .7% . Conclusion: Machine learning algorithms can be effectively utilized to build an accurate pediatric mortality prediction model that leverages variables easily obtained upon trauma admission.  \nKEYWORDS  \nmachine learning, trauma, mortality, prediction, pediatrics  \nIntroduction  \nPediatric trauma is a significant global health challenge, contributing substantially to mortality and disability among children (1) . The treatment of pediatric patients, who differ markedly from adults in their physiological, developmental, and psychological characteristics, requires a tailored approach. Despite advancements in pediatric trauma care, there remains a critical need for more refined risk stratification models to better predict patient outcomes and guide interventions. Current assessment measures often focus on immediate clinical parameters and outcomes; however, the incorporation of broader considerations, including post-discharge recovery, psychological support, and long-term rehabilitation, remains largely unexplored (2) .  \nThe complexities of pediatric trauma recovery challenge clinicians to identify ways to improve functional outcomes, one such method ","cbCaigwjDL6PotVr","https://ap.wps.com/l/cbCaigwjDL6PotVr","pdf",9763721,1,"English","en",105,"# Background\n## Pediatric trauma risk stratification needs\n## Role of machine learning in outcome prediction\n# Methods\n## Study design and setting\n## Data source and cohort split\n## Model training and validation\n# Results\n## Mortality rate and cohort characteristics\n## Model discrimination and accuracy\n# Conclusion","[{\"question\":\"What was the main objective of this study on pediatric trauma patients?\",\"answer\":\"To develop a machine learning predictive model for mortality outcomes in pediatric trauma patients, identifying clinical and physiologic predictors available at admission.\"},{\"question\":\"How were the machine learning models built and evaluated?\",\"answer\":\"The data were split into a 70% development cohort to build four ML models and a 30% validation cohort to test them. Performance was assessed using AUC from receiver operating characteristic curves.\"},{\"question\":\"Which model performed best and how well did it discriminate mortality risk?\",\"answer\":\"XGBoost showed the highest performance, with the AUCs across the four models ranging from 92.7 to 97.7 and the highest accuracy reported at 97.7%.\"}]","Machine learning-based prediction of mortality in pediatric trauma patients | PDF",1785897210,20,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":27},"machine-learning-based-prediction-of-mortality-in-pediatric-trauma-patients","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"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":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-based-prediction-of-mortality-in-pediatric-trauma-patients/125174/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What was the main objective of this study on pediatric trauma patients?","Question",{"text":74,"@type":75},"To develop a machine learning predictive model for mortality outcomes in pediatric trauma patients, identifying clinical and physiologic predictors available at admission.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How were the machine learning models built and evaluated?",{"text":79,"@type":75},"The data were split into a 70% development cohort to build four ML models and a 30% validation cohort to test them. Performance was assessed using AUC from receiver operating characteristic curves.",{"name":81,"@type":72,"acceptedAnswer":82},"Which model performed best and how well did it discriminate mortality risk?",{"text":83,"@type":75},"XGBoost showed the highest performance, with the AUCs across the four models ranging from 92.7 to 97.7 and the highest accuracy reported at 97.7%.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]