[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128453-en":3,"doc-seo-128453-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128453,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters - Original Research","Successful weaning from mechanical ventilation is critical for patients in intensive care units, yet reliable models for real-time extubation prediction remain limited. This study develops a machine-learning approach using only time-series ventilator-derived parameters to forecast successful extubation. Retrospective data from mechanically ventilated patients are used, with recursive feature elimination for important variables and SMOTE to address class imbalance. Model performance is assessed with 10-fold cross-validation using AUC, F1, and accuracy, showing strong predictive results.","TYPE Original Research PUBLISHED 09 May 2023  \nDOI 10.3389/fmed.2023.1167445  \nOPEN ACCESS  \nEDITED BY  \nMd. Mohaimenul Islam,  \nThe Ohio State University, United States  \nREVIEWED BY  \nHosna Salmani,  \nIran University of Medical Sciences, Iran Marlin Ramadhan Baidillah,  \nNational Research and Innovation Agency (BRIN), Indonesia  \n*CORRESPONDENCE  \nJia-Lang Xu  \n [jlxu.academy@gmail.com](jlxu.academy@gmail.com)[ ](jlxu.academy@gmail.com)Ming-Hon Hou  \n [mhho@nchu.edu.tw](mhho@nchu.edu.tw)  \n†These authors have contributed equally to this work  \nRECEIVED 16 February 2023  \nACCEPTED 17 April 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nHuang K-Y, Hsu Y-L, Chen H-C, Horng M-H, Chung C-L, Lin C-H, Xu J-L and Hou M-H (2023) Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.  \nFront. Med. 10:1167445 .  \ndoi: 10.3389/fmed.2023.1167445  \nCOPYRIGHT  \n© 2023 Huang, Hsu, Chen, Horng, Chung, Lin, Xu and Hou. 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.  \nDeveloping a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series  \nventilator-derived parameters  \nKuo-Yang Huang 1, 2, 3, 4, Ying-Lin Hsu 5, Huang-Chi Chen 6, Ming-Hwarng Horng 6, Che-Liang Chung 6, Ching-Hsiung Lin 1, 3, 7, Jia-Lang Xu 2*† and Ming-Hon Hou 1, 3, 4, 8, 9*†  \n1 Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan, 2Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan,  \n3 Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan,  \n4 Ph. D. Program in Medical Biotechnology, National Chung Hsing University, Taichung, Taiwan,  \n5 Department of Applied Mathematics, Institute of Statistics, National Chung Hsing University, Taichung, Taiwan, 6 Division of Chest Medicine, Department of Internal Medicine, Yuanlin Christian Hospital, Changhua, Taiwan, 7 Department of Recreation and Holistic Wellness, MingDao University, Changhua, Taiwan, 8Graduate Institute of Biotechnology, National Chung Hsing University, Taichung, Taiwan, 9 Department of Life Sciences, National Chung Hsing University, Taichung, Taiwan  \nBackground: Successful weaning from mechanical ventilation is important for patients admitted to intensive care units. However, models for predicting realtime weaning outcomes remain inadequate. Therefore, this study aimed to develop a machine-learning model for predicting successful extubation only using time-series ventilator-derived parameters with good accuracy.  \nMethods: Patients with mechanical ventilation admitted to the Yuanlin Christian Hospital in Taiwan between August 2015 and November 2020 were retrospectively included. A dataset with ventilator-derived parameters was obtained before extubation. Recursive feature elimination was applied to select the most important features. Machine-learning models of logistic regression, random forest (RF), and support vector machine were adopted to predict extubation outcomes. In addition, the synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem. The area under the receiver operating characteristic (AUC), F1 score, and accuracy, along with the 10-fold crossvalidation, were used to evaluate prediction performance.  \nResults: In this study, 233 patients were included, of whom 28 (12 .0%) failed extubation. The six ventilatory variables per 180s dataset had optimal feature imp","cbCailXSNojRxU7B","https://ap.wps.com/l/cbCailXSNojRxU7B","pdf",962172,4,1,9,"English","en",105,"# Introduction\n## Background and clinical need\n## Existing predictors and limitations\n# Methods\n## Study design and dataset\n## Feature selection and modeling\n## Evaluation metrics\n# Results\n## Patient cohort and failure rate\n## Performance of models and feature importance\n# Conclusion","[{\"question\":\"What was the main goal of this study?\",\"answer\":\"To develop a machine-learning model that predicts successful extubation in mechanically ventilated patients using only time-series ventilator-derived parameters.\"},{\"question\":\"How were predictive features selected and data imbalance handled?\",\"answer\":\"Recursive feature elimination selected the most important ventilatory variables, and SMOTE was used to address imbalance in extubation outcomes.\"},{\"question\":\"Which machine-learning model performed best and how was it evaluated?\",\"answer\":\"The random forest model performed best, evaluated using AUC, F1 score, accuracy, and 10-fold cross-validation.\"}]","Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters - Original Research | PDF",1786001148,23,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"developing-a-machine-learning-model-for-real-time-prediction-of-successful-extubation-in-mechanically-ventilated-patients-using-time-series-ventilator-derived-parameters-original-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/healthcare/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/developing-a-machine-learning-model-for-real-time-prediction-of-successful-extubation-in-mechanically-ventilated-patients-using-time-series-ventilator-derived-parameters-original-research/128453/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main goal of this study?","Question",{"text":76,"@type":77},"To develop a machine-learning model that predicts successful extubation in mechanically ventilated patients using only time-series ventilator-derived parameters.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were predictive features selected and data imbalance handled?",{"text":81,"@type":77},"Recursive feature elimination selected the most important ventilatory variables, and SMOTE was used to address imbalance in extubation outcomes.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning model performed best and how was it evaluated?",{"text":85,"@type":77},"The random forest model performed best, evaluated using AUC, F1 score, accuracy, and 10-fold cross-validation.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":47,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]