[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128482-en":3,"doc-seo-128482-105":30,"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":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},128482,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Artificial Intelligence for the Prediction of Weaning Readiness Outcome in a Multi-centrical Clinical Cohort of Mechanically Ventilated Patients - Tesi di Laurea","Mechanical ventilation is used to manage acute respiratory failure until independent breathing is possible, with daily clinical screening to decide whether it can be safely stopped. The process involves Readiness Testing (RT) followed by a 30-minute spontaneous breathing trial (SBT), yielding three mutually exclusive daily scenarios: SBT not attempted, SBT failure, or SBT success. This thesis develops a deep-learning, multi-source neural network to predict these outcomes early in the morning using clinical data, prior-day diary information, and minute-by-minute ventilator parameter histories from a retrospective multicenter cohort in Italy over 27 months, optimizing hyperparameters via cross-validation and evaluating final performance on held-out test patients.","UNIVERSITÀ DEGLI STUDI DI PADOVA  \nCorso di Laurea Magistrale a ciclo unicoin Medicina e Chirurgia  \nDIPARTIMENTO DI SCIENZE CARDIO-TORACO-VASCOLARI E SANITÀ PUBBLICA  \nDirettore: Prof. Federico Rea  \nUNITÀ DI BIOSTATISTICA, EPIDEMIOLOGIA E SALUTE PUBBLICA  \nDirettore: Prof. Dario Gregori  \nTESI DI LAUREA  \nArtificial Intelligence for the prediction of weaning readiness outcome in a multi-centrical clinical cohort of mechanically ventilated patients  \nRelatore:  \nCorrado Lanera, Ph.D.  \nLaureando/a:  \nAndrea Pedot  \nANNO ACCADEMICO 2022/2023  \nUNIVERSITÀ DEGLI STUDI DI PADOVA  \nCorso di Laurea Magistrale a ciclo unicoin Medicina e Chirurgia  \nDIPARTIMENTO DI SCIENZE CARDIO-TORACO-VASCOLARI E SANITÀ PUBBLICA  \nDirettore: Prof. Federico Rea  \nUNITÀ DI BIOSTATISTICA, EPIDEMIOLOGIA E SALUTE PUBBLICA  \nDirettore: Prof. Dario Gregori  \nTESI DI LAUREA  \nArtificial Intelligence for the prediction of weaning readiness outcome in a multi-centrical clinical cohort of mechanically ventilated patients  \nRelatore:  \nCorrado Lanera, Ph.D.  \nLaureando/a:  \nAndrea Pedot  \nTABLE OF CONTENTS  \nTable of Contents ............................................................................................................ 1  \nAbstract [structured summary]....................................................................................... 2  \nRiass unto ......................................................................................................................... 3  \nIntroduction .................................................................................................................... 4  \nRationale ..................................................................................................................... 4  \nObjectives.................................................................................................................... 7  \nMethods .......................................................................................................................... 8  \nSetting ......................................................................................................................... 8  \nPrediction problem definition ................................................................................... 11  \nData preparation ....................................................................................................... 14  \nPrediction model building ......................................................................................... 22  \nModel Selection ........................................................................................................ 25  \nResults ........................................................................................................................... 27  \nPreliminary models performance ............................................................................. 27  \nFinal model performance .......................................................................................... 28  \nDiscussion...................................................................................................................... 33  \nLimitations of the model ........................................................................................... 34  \nClinical implications of model adoption.................................................................... 36  \nInterpretability and Explainability of the model ....................................................... 36  \nClinical implications of broader AI tools adoption .................................................... 37  \nConclusions ................................................................................................................... 40  \nBib liografia .................................................................................................................... 41  \nSupplementary material ............................................................................................... 49  \n1  \nABSTR","cbCaia8abwWbyEqn","https://ap.wps.com/l/cbCaia8abwWbyEqn","pdf",5440589,1,89,"English","en",105,"# Abstract\n## Structured summary\n# Introduction\n## Rationale\n## Objectives\n# Methods\n## Setting\n## Prediction problem definition\n## Data preparation\n## Prediction model building\n## Model selection\n# Results\n## Preliminary models performance\n## Final model performance\n# Discussion\n## Limitations of the model\n## Clinical implications of model adoption\n## Interpretability and explainability of the model\n## Clinical implications of broader AI tools adoption\n# Conclusions\n# Bib liografia\n# Supplementary material","[{\"question\":\"How does the mechanical ventilation weaning decision process work in this study?\",\"answer\":\"Patients undergo Readiness Testing (RT) followed by a 30-minute spontaneous breathing trial (SBT) if RT is successful. Depending on RT and SBT results, the day falls into one of three mutually exclusive scenarios: SBT not attempted, SBT failure, or SBT success.\"},{\"question\":\"What data sources are used to make the early-morning AI predictions?\",\"answer\":\"Predictions are based on clinical data, information from the previous day’s clinical diary, and whole minute-by-minute recording histories of mechanical ventilator parameters collected from a retrospective observational multicenter study.\"},{\"question\":\"How is the final deep-learning model evaluated and what is its reported performance?\",\"answer\":\"Hyperparameters are optimized via cross-validation, with 36 out of 182 patients held out for testing. The final AI model reports 79% accuracy and improves on comparison models such as XG Boost trained on daily and baseline data from the previous day.\"}]","Artificial Intelligence for the Prediction of Weaning Readiness Outcome in a Multi-centrical Clinical Cohort of Mechanically Ventilated Patients - Tesi di Laurea | PDF",1786001313,224,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"artificial-intelligence-for-the-prediction-of-weaning-readiness-outcome-in-a-multi-centrical-clinical-cohort-of-mechanically-ventilated-patients-graduation-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@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/artificial-intelligence-for-the-prediction-of-weaning-readiness-outcome-in-a-multi-centrical-clinical-cohort-of-mechanically-ventilated-patients-graduation-thesis/128482/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","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},"How does the mechanical ventilation weaning decision process work in this study?","Question",{"text":76,"@type":77},"Patients undergo Readiness Testing (RT) followed by a 30-minute spontaneous breathing trial (SBT) if RT is successful. Depending on RT and SBT results, the day falls into one of three mutually exclusive scenarios: SBT not attempted, SBT failure, or SBT success.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources are used to make the early-morning AI predictions?",{"text":81,"@type":77},"Predictions are based on clinical data, information from the previous day’s clinical diary, and whole minute-by-minute recording histories of mechanical ventilator parameters collected from a retrospective observational multicenter study.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the final deep-learning model evaluated and what is its reported performance?",{"text":85,"@type":77},"Hyperparameters are optimized via cross-validation, with 36 out of 182 patients held out for testing. The final AI model reports 79% accuracy and improves on comparison models such as XG Boost trained on daily and baseline data from the previous day.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]