[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121619-en":3,"doc-seo-121619-105":30,"detail-sidebar-cat-0-en-105":95},{"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":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},121619,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Predicting Pneumonia During Hospitalization in Flail Chest Patients Using Machine Learning Approaches - Original Research","Pneumonia is a frequent, high-morbidity complication in flail chest patients, and current identification methods often lack accuracy, risking delays in antimicrobial treatment. A retrospective analysis of adult flail chest cases builds and compares multiple machine-learning models to predict pneumonia risk from electronic medical records. The best-performing XGBoost model is interpreted with SHAP to highlight key predictors, improving clinical relevance for early risk assessment and timely preventive care.","TYPE Original Research PUBLISHED 06 January 2023  \nDOI 10.3389/fsurg.2022.1060691  \nEDITED BY  \nMarco Scarci,  \nHammersmith Hospital, United Kingdom  \nREVIEWED BY  \nSavvas Lampridis,  \nHammersmith Hospital, United Kingdom Paolo Albino Ferrari,  \nOspedale Oncologico Armando Businco, Italy  \n*CORRESPONDENCE  \nDingyuan Du[dudingyuan@qq.com](dudingyuan@qq.com)[ ](dudingyuan@qq.com)Lingyun Zou [lingyun.zou@gmail.com](lingyun.zou@gmail.com)  \nSPECIALTY SECTION  \nThis article was submitted to Thoracic Surgery, a section of the journal Frontiers in Surgery  \nRECEIVED 03 October 2022  \nACCEPTED 14 November 2022  \nPUBLISHED 06 January 2023  \nCITATION  \nSong X, Li H, Chen Q, Zhang T, Huang G, Zou Land Du D (2023) Predicting pneumonia during hospitalization in flail chest patients using machine learning approaches.  \nFront. Surg. 9:1060691 .  \ndoi: 10.3389/fsurg.2022.1060691  \nCOPYRIGHT  \n© 2023 Song, Li, Chen, Zhang, Huang, Zou and Du. 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.  \nPredicting pneumonia during hospitalization in flail chest patients using machine learning approaches  \nXiaolin Song1,2, Hui Li2, Qingsong Chen2, Tao Zhang1,2, Guangbin Huang2, Lingyun Zou3* and Dingyuan Du2*  \n1School of Medicine, Chongqing University, Chongqing, China, 2Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China, 3Clinical Data Research Center, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China  \nObjective: Pneumonia is a common pulmonary complication of flail chest, causing high morbidity and mortality rates in affected patients. The existing methods for identifying pneumonia have low accuracy, and their use may delay antimicrobial therapy. However, machine learning can be combined with electronic medical record systems to identify information and assist in quick clinical decision-making. Our study aimed to develop a novel machine-learning model to predict pneumonia risk in flail chest patients. Methods: From January 2011 to December 2021, the electronic medical records of 169 adult patients with flail chest at a tertiary teaching hospital in an urban level I Trauma Centre in Chongqing were retrospectively analysed. Then, the patients were randomly divided into training and test sets at a ratio of 7:3 . Using the Fisher score, the best subset of variables was chosen. The performance of the seven models was evaluated by computing the area under the receiver operating characteristic curve (AUC) . The output of the XGBoost model was shown using the Shapley Additive exPlanation (SHAP) method.  \nResults: Of 802 multiple rib fracture patients, 169 flail chest patients were eventually included, and 86 (50 . 80%) were diagnosed with pneumonia. The XGBoost model performed the best among all seven machine-learning models. The AUC of the XGBoost model was 0 .895 (sensitivity: 84 .3%; specificity: 80 . 0%) .  \nPneumonia in flail chest patients was associated with several features: systolic blood pressure, pH value, blood transfusion, and ISS.  \nConclusion: Our study demonstrated that the XGBoost model with 32 variables had high reliability in assessing risk indicators of pneumonia in flail chest patients. The SHAP method can identify vital pneumonia risk factors, making the XGBoost model’s output clinically meaningful.  \nKEYWORDS  \nflail chest, pneumonia, machine learning, risk factors, extreme gradient boosting  \nIntroduction  \nFlail chest is the most severe type of chest trauma, is found in approximately 4% of patients with rib fractures and is defined as at least ","cbCaitEbB9Eqmgll","https://ap.wps.com/l/cbCaitEbB9Eqmgll","pdf",1202914,1,11,"English","en",105,"# Objective\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Background and clinical need\n## Existing prediction tools and limitations","[{\"question\":\"Why is pneumonia prediction important in flail chest patients?\",\"answer\":\"Pneumonia is common and severe in flail chest, and inaccurate identification can lead to delayed or inappropriate antibiotics, increasing morbidity and mortality.\"},{\"question\":\"How was the pneumonia prediction model developed and evaluated?\",\"answer\":\"Electronic medical records from 2011 to 2021 were retrospectively analyzed, then split into training and test sets. Feature selection used the Fisher score, and model performance was assessed using AUC for seven machine-learning approaches.\"},{\"question\":\"Which model performed best and what were its key metrics?\",\"answer\":\"The XGBoost model performed best among the seven models, with AUC 0.895, sensitivity 84.3%, and specificity 80.0%.\"},{\"question\":\"How were important risk factors identified?\",\"answer\":\"The study used SHAP to interpret the XGBoost output, linking pneumonia risk to variables including systolic blood pressure, pH value, blood transfusion, and ISS.\"}]","Predicting Pneumonia During Hospitalization in Flail Chest Patients Using Machine Learning Approaches - Original Research | PDF",1785805688,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"predicting-pneumonia-during-hospitalization-in-flail-chest-patients-using-machine-learning-approaches-original-research","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-pneumonia-during-hospitalization-in-flail-chest-patients-using-machine-learning-approaches-original-research/121619/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is pneumonia prediction important in flail chest patients?","Question",{"text":75,"@type":76},"Pneumonia is common and severe in flail chest, and inaccurate identification can lead to delayed or inappropriate antibiotics, increasing morbidity and mortality.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the pneumonia prediction model developed and evaluated?",{"text":80,"@type":76},"Electronic medical records from 2011 to 2021 were retrospectively analyzed, then split into training and test sets. Feature selection used the Fisher score, and model performance was assessed using AUC for seven machine-learning approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what were its key metrics?",{"text":84,"@type":76},"The XGBoost model performed best among the seven models, with AUC 0.895, sensitivity 84.3%, and specificity 80.0%.",{"name":86,"@type":73,"acceptedAnswer":87},"How were important risk factors identified?",{"text":88,"@type":76},"The study used SHAP to interpret the XGBoost output, linking pneumonia risk to variables including systolic blood pressure, pH value, blood transfusion, and ISS.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]