[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123088-en":3,"doc-seo-123088-105":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":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},123088,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning","Patients with diabetes carry a higher likelihood of postoperative pulmonary infection (PPI), yet accurate, diabetes-specific prediction models remain limited. This retrospective study develops and validates a machine learning approach to estimate PPI risk in 1,269 diabetic patients undergoing elective non-cardiac, non-neurological surgeries. Nine algorithms are compared, with LASSO-based feature selection and performance evaluation using AUC, precision, accuracy, specificity, and F1-score.","TYPE Original Research PUBLISHED 04 December 2024 DOI 10.3389/fphys.2024.1501854  \nOPEN ACCESS  \nEDITED BY  \nRajesh Kumar Tripathy,  \nBirla Institute of Technology and Science, India  \nREVIEWED BY  \nChang Won Jeong,  \nWonkwang University, Republic of Korea Zhe Sang,  \nIcahn School of Medicine at Mount Sinai, United States  \nDan Xue,  \nUniversity of Pittsburgh, United States  \n*CORRESPONDENCE  \nShun Wang,  \n [wangshun@cmc.edu.cn](wangshun@cmc.edu.cn)[ ](wangshun@cmc.edu.cn)Qiaoli Liu,  \n [1176620592@qq.com](1176620592@qq.com)  \n‡These authors have contributed equally to this work  \nRECEIVED 30 September 2024  \nACCEPTED 21 November 2024  \nPUBLISHED 04 December 2024  \nCITATION  \nZhao C, Xiang B, Zhang J, Yang P, Liu Q and Wang S (2024) Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning.  \nFront. Physiol. 15:1501854 .  \ndoi: 10.3389/fphys.2024.1501854  \nCOPYRIGHT  \n© 2024 Zhao, Xiang, Zhang, Yang, Liu and Wang. 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 postoperative pulmonary infection risk inpatients with diabetes using machine learning  \nChunxiu Zhao 1‡, Bingbing Xiang 􀀁 2‡, Jie Zhang 3‡, Pingliang Yang 3‡, Qiaoli Liu 3* and Shun Wang 3*  \n1Department of Critical Care Medicine, Affiliated Hospital of Southwest Jiaotong University, The Third People ’s Hospital of Chengdu, Chengdu, Sichuan, China, 2Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China, 3Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China  \nBackground: Patients with diabetes face an increased risk of postoperative pulmonary infection (PPI) . However, precise predictive models specific to this patient group are lacking.  \nObjective: To develop and validate a machine learning model for predicting PPI risk in patients with diabetes.  \nMethods: This retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023 . Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Model performance was assessed via the Area Under the Curve (AUC), precision, accuracy, specificity and F1-score.  \nResults: The Ada Boost classifier (ADA) model exhibited the best performance with an AUC of 0.901, Accuracy of 0.91, Precision of 0.82, specificity of 0.98, PPV of 0 . 82, and NPV of 0 .82. LASSO feature selection identified six optimal predictive factors: postoperative transfer to the ICU, Age, American Society of Anesthesiologists (ASA) physical status score, chronic obstructive pulmonary disease (COPD) status, surgical department, and duration of surgery.  \nAbbreviations: PPI, Postoperative Pulmonary Infection; ICU, Intensive Care Unit; PCA, PatientControlled Analgesia; ASA, American Society of Anesthesiologists; COPD, Chronic Obstructive Pulmonary Disease; NYHA, New York Heart Association; LASSO, Least Absolute Shrinkage and Selection Operator; AUC, Area Under the Curve; ROC, Receiver Operating Characteristic; PPV, Positive Predictive Value; NPV, Negative Predictive Value; KNN, K-Nearest Neighbors; SVM, Support Vector Machine; RF, Random Forest; DT, Decision Tree; LightGBM, Light Gradient Boosting Machine; ADA, Ada Boost; NB, Naive Bayes; LR, Logistic Regression; LDA, Linear Discriminant Analysis; SHAP, SHapley Additive exPlanations; SD, Standard Deviation; IQR, In","cbCaigvt3YlGtQDT","https://ap.wps.com/l/cbCaigvt3YlGtQDT","pdf",1930550,1,10,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What was the study objective for patients with diabetes?\",\"answer\":\"To develop and validate a machine learning model that predicts postoperative pulmonary infection (PPI) risk in patients with diabetes.\"},{\"question\":\"How were predictive models built and evaluated?\",\"answer\":\"Nine machine learning algorithms were tested, with LASSO logistic regression for feature selection and model performance assessed using AUC, precision, accuracy, specificity, and F1-score.\"},{\"question\":\"Which model performed best and what features were selected?\",\"answer\":\"The Ada Boost classifier showed the best performance (AUC 0.901). LASSO identified six key predictive factors: ICU transfer after surgery, age, ASA physical status, COPD status, surgical department, and duration of surgery.\"}]","Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning | PDF",1785814585,25,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-postoperative-pulmonary-infection-risk-in-patients-with-diabetes-using-machine-learning","",{"@graph":36,"@context":85},[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-postoperative-pulmonary-infection-risk-in-patients-with-diabetes-using-machine-learning/123088/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What was the study objective for patients with diabetes?","Question",{"text":75,"@type":76},"To develop and validate a machine learning model that predicts postoperative pulmonary infection (PPI) risk in patients with diabetes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were predictive models built and evaluated?",{"text":80,"@type":76},"Nine machine learning algorithms were tested, with LASSO logistic regression for feature selection and model performance assessed using AUC, precision, accuracy, specificity, and F1-score.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what features were selected?",{"text":84,"@type":76},"The Ada Boost classifier showed the best performance (AUC 0.901). LASSO identified six key predictive factors: ICU transfer after surgery, age, ASA physical status, COPD status, surgical department, and duration of surgery.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]