[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121213-en":3,"doc-seo-121213-105":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":4,"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},121213,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting 30-day Mortality in Severely Injured Elderly Patients with Trauma in Korea Using Machine Learning Algorithms - a Retrospective Study","Accurate mortality prediction is needed as the number of elderly trauma patients continues to rise, enabling better clinical decision-making. This retrospective study developed and compared machine learning predictive models for 30-day mortality in severely injured patients aged 65 years or older (ISS ≥15) treated at a regional trauma center from 2016–2022. Four models were evaluated with discrimination and classification metrics and interpreted using SHAP, while learning curves assessed generalization and robustness.","Original Article  \nJ Trauma Inj 2024;37(3):201-208 [https://doi.org/10.20408/jti.2024.0024](https://doi.org/10.20408/jti.2024.0024)  \npISSN 2799-4317 • eISSN 2287-1683  \nPredicting 30-day mortality in severely injured elderly patients with trauma in Korea using machine learning algorithms: a retrospective study  \nJonghee Han, MD1 , Su Young Yoon, MD1 , Junepill Seok, MD2 , Jin Young Lee, MD2 , Jin Suk Lee, MD2 , Jin Bong Ye, MD2 , Younghoon Sul, MD2,3 , Se Heon Kim, MD2 , Hong Rye Kim, MD4   \n1Department of Cardiovascular and Thoracic Surgery, Trauma Center, Chungbuk National University Hospital, Cheongju, Korea 2 Department of Trauma Surgery, Trauma Center, Chungbuk National University Hospital, Cheongju, Korea  \n3 Department of Trauma Surgery, Chungbuk National University College of Medicine, Cheongju, Korea 4 Department of Neurosurgery, Trauma Center, Chungbuk National University Hospital, Cheongju, Korea  \nReceived: April 22, 2024  \nRevised: May 23, 2024  \nAccepted: May 29, 2024  \nCorrespondence to  \nJonghee Han, MD  \nDepartment of Cardiovascular and Thoracic Surgery, Trauma Center, Chungbuk National University Hospital, 776 1sunhwan-ro, Seowon-gu, Cheongju 28644, Korea  \nTel: +82-43-269-7849  \n[Email: medihan7@naver.com](Email: medihan7@naver.com)  \nPurpose: The number of elderly patients with trauma is increasing; therefore, precise models are necessary to estimate the mortality risk of elderly patients with trauma for informed clinical decision-making. This study aimed to develop machine learning based predictive models that predict 30-day mortality in severely injured elderly patients with trauma and to compare the predictive performance of various machine learning models.  \nMethods: This study targeted patients aged ≥65 years with an Injury Severity Score of ≥15 who visited the regional trauma center at Chungbuk National University Hospital between 2016 and 2022. Four machine learning models—logistic regression, decision tree, random forest, and eXtreme Gradient Boosting (XGBoost)—were developed to predict 30-day mortality. The models’ performance was compared using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, specificity, F1 score, as well as Shapley Additive Explanations (SHAP) values and learning curves.  \nResults: The performance evaluation of the machine learning models for predicting mortality in severely injured elderly patients with trauma showed AUC values for logistic regression, decision tree, random forest, and XGBoost of 0.938, 0.863, 0.919, and 0.934, respectively. Among the four models, XGBoost demonstrated superior accuracy, precision, recall, specificity, and F1 score of 0.91, 0.72, 0.86, 0.92, and 0.78, respectively. Analysis of important features of XGBoost using SHAP revealed associations such as a high Glasgow Coma Scale negatively impacting mortality probability, while higher counts of transfused red blood cells were positively correlated with mortality probability. The learning curves indicated increased generalization and robustness as training examples increased. Conclusions: We showed that machine learning models, especially XGBoost, can be used to predict 30-day mortality in severely injured elderly patients with trauma.  \nPrognostic tools utilizing these models are helpful for physicians to evaluate the risk of mortality in elderly patients with severe trauma.  \nKeywords: Wounds and injuries; Aged; Mortality; Prediction model; Machine learning  \n© 2024 The Korean Society of Traumatology  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.  \n[https://doi.org/10.20408/jti.2024.0024](https://doi.org/10.20408/jti.2024.0024) [www.jtr","cbCaidhUZEy0SFNS","https://ap.wps.com/l/cbCaidhUZEy0SFNS","pdf",943741,1,"English","en",105,"# Purpose\n# Methods\n## Study population\n## Machine learning models and evaluation\n# Results\n## Model performance\n## Feature interpretation (SHAP)\n## Learning curves\n# Conclusions\n# Keywords","[{\"question\":\"What was the aim of the study on elderly trauma patients in Korea?\",\"answer\":\"To develop machine learning–based models that predict 30-day mortality in severely injured elderly trauma patients and to compare the predictive performance of different algorithms.\"},{\"question\":\"What patient group and time period were included in the retrospective analysis?\",\"answer\":\"Patients aged 65 years or older with an Injury Severity Score of at least 15 who visited a regional trauma center at Chungbuk National University Hospital between 2016 and 2022.\"},{\"question\":\"Which machine learning model performed best for predicting 30-day mortality?\",\"answer\":\"XGBoost showed the superior overall classification performance among the four models, with the highest accuracy, precision, recall, specificity, and F1 score reported in the results.\"}]","Predicting 30-day Mortality in Severely Injured Elderly Patients with Trauma in Korea Using Machine Learning Algorithms - a Retrospective Study | PDF",1785734381,20,{"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},"predicting-30-day-mortality-in-severely-injured-elderly-patients-with-trauma-in-korea-using-machine-learning-algorithms-a-retrospective-study","",{"@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/predicting-30-day-mortality-in-severely-injured-elderly-patients-with-trauma-in-korea-using-machine-learning-algorithms-a-retrospective-study/121213/",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","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What was the aim of the study on elderly trauma patients in Korea?","Question",{"text":74,"@type":75},"To develop machine learning–based models that predict 30-day mortality in severely injured elderly trauma patients and to compare the predictive performance of different algorithms.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What patient group and time period were included in the retrospective analysis?",{"text":79,"@type":75},"Patients aged 65 years or older with an Injury Severity Score of at least 15 who visited a regional trauma center at Chungbuk National University Hospital between 2016 and 2022.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning model performed best for predicting 30-day mortality?",{"text":83,"@type":75},"XGBoost showed the superior overall classification performance among the four models, with the highest accuracy, precision, recall, specificity, and F1 score reported in the results.","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"]