[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123434-en":3,"doc-seo-123434-105":30,"detail-sidebar-cat-0-en-105":91},{"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":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},123434,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine Learning to Predict the Severity Score of Trauma Pediatric Patients - Research Abstract","Study aims to address uncertainty about existing trauma scoring systems in pediatric patients, particularly their validity and their role in guiding treatment. Many current tools depend on variables available days after admission, limiting the ability to predict severity in the first hours after injury. This work develops a pediatric trauma severity approach using variables obtainable in the pre-hospital setting and at admission, with the intent to help determine the minimum required level of care to support survival.","Wayne State University  \n\n| Medical Student Research Symposium | School of Medicine |\n| --- | --- |\n| April 2025\u003Cbr>Machine Learning to Predict the Severity Score of Trauma Pediatric Patients\u003Cbr>Ashley Frei BS\u003Cbr>Wayne State University School of Medicine, [hl8088@wayne.edu](hl8088@wayne.edu)\u003Cbr>Elika Ridelman PhD\u003Cbr>Department of Surgery, Division of Pediatric Surgery, Children’s Hospital of Michigan/Wayne State University School of Medicine\u003Cbr>Rebecca Adams BS\u003Cbr>Wayne State University School of Medicine, [rebecca.adams3@med.wayne.edu](rebecca.adams3@med.wayne.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.wayne.edu/som_srs](https://digitalcommons.wayne.edu/som_srs)\u003Cbr> Part of the Medicine and Health Sciences Commons |  |\n\nRecommended Citation  \nFrei, Ashley BS; Ridelman, Elika PhD; and Adams, Rebecca BS, \"Machine Learning to Predict the Severity Score of Trauma Pediatric Patients\" (2025) . Medical Student Research Symposium. 392.  \n[https://digitalcommons.wayne.edu/som_srs/392](https://digitalcommons.wayne.edu/som_srs/392)  \nThis Research Abstract is brought to you for free and open access by the School of Medicine at DigitalCommons@WayneState. It has been accepted for inclusion in Medical Student Research Symposium by an authorized administrator of DigitalCommons@WayneState.  \nMachine Learning to Predict the Severity Score of Trauma Pediatric Patients Ashley Frei BS, Rebecca Adams BS, Elika Ridelman PhD  \nBackground  \nMany scoring systems currently exist to predict severity and mortality among trauma patients. However, controversy remains over the validity of these scoring systems in pediatrics and their use in guiding treatment. Additionally, many of these tools utilize variables that can only be collected days after admission, prohibiting predictions from being determined in the immediate hours following the trauma. The goal of this study is to develop an appropriate scoring system utilizing variables collected in the pre-hospital setting and on admission of a trauma pediatric patient that could be used to help determine the minimal necessary level of care for survival.  \nMethods  \nA retrospective chart review was performed for 1277 pediatric patients presenting to an innercity Level 1 trauma hospital via emergency medical services (EMS) status post motor vehicle collision. Many variables were collected from the EMS documentation, including the restraintsin use, position in vehicle, location of the accident, and pre-hospital Glasgow coma scale (GCS) . Additionally, variables were collected from the medical charts including admission vital signs, mechanism and speed of the collision, as well as injuries sustained and procedures performed in the operating room (OR) . This data was then provided to the Human-Interactive Robotics (HIRo) Lab at Purdue University to analyze with their machine learning technology.  \nResults and Conclusion  \nData is currently being analyzed by the HIRo machine learning lab at Purdue University. Analysis and conclusions will be completed in time to be presented at the symposium in March.  \nKey words:  \nTrauma, pediatrics, machine learning, mortality, morbidity, trauma severity","cbCainjFmmkxVqnN","https://ap.wps.com/l/cbCainjFmmkxVqnN","pdf",121209,1,2,"English","en",105,"# Background\n## Current pediatric trauma scoring limitations\n## Need for early prediction\n# Methods\n## Retrospective chart review\n## Data sources and variables\n## Machine learning analysis pipeline\n# Results and Conclusion\n## Ongoing analysis timeline\n# Key Words","[{\"question\":\"What problem does this study target in pediatric trauma care?\",\"answer\":\"It targets controversy over how valid trauma scoring systems are for pediatrics and how they can guide treatment.\"},{\"question\":\"Why is early prediction a key limitation of existing scoring systems?\",\"answer\":\"Many tools rely on variables that are only available days after admission, preventing severity prediction in the immediate hours after trauma.\"},{\"question\":\"What data sources are used to build the new severity scoring approach?\",\"answer\":\"Variables are collected from EMS documentation and from medical charts at admission, including vital signs, mechanism and speed, injuries, and procedures.\"}]","Machine Learning to Predict the Severity Score of Trauma Pediatric Patients - 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