[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121796-en":3,"doc-seo-121796-105":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":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},121796,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study","Improved survival in paediatric critical care still leaves clinicians without reliable ways to forecast long-term school outcomes. A population-based study of children under 16 years admitted to an intensive care unit in Queensland, Australia (1997–2019) developed a machine learning model using routine ICU data. Educational failure was defined as not meeting National Minimum Standards on NAPLAN across primary and secondary school. Among 13,957 survivors, the model achieved AUROC 0.8 (SD 0.01) and meaningful sensitivity, with socio-economic status, illness severity, and neurological, congenital, and genetic comorbidities driving predictions.","Zurich Open Repository and Archive  \nUniversity of Zurich  \nUniversity Library Strickhofstrasse 39  \nCH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch)  \nYear: 2023  \nMachine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study  \nGilholm, Patricia ; Gibbons, Kristen ; Brüningk, Sarah ; Klatt, Juliane ; Vaithianathan, Rhema ; Long, Debbie ; Millar, Johnny ; Tomaszewski, Wojtek ; Schlapbach, LuregnJ  \nAbstract: Purpose: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU) . Methods: Population-based study of children \u003C 16 years requiring ICU admission in Queensland, Australia, between 1997 and 2019 . Failure to meet the National Minimum Standard (NMS) in the National Assessment Program-Literacy and Numeracy (NAPLAN) assessment during primary and secondary school was the primary outcome. Routine ICU information was used to train machine learning classifiers. Models were trained, validated and tested using stratified nested cross-validation. Results: 13,957 childhood ICU survivors with 37,200 corresponding NAPLAN tests after a median follow-up duration of 6 years were included. 14.7%, 17%, 15.6% and 16.6% failed to meet NMS in school grades 3, 5, 7 and 9 . The model demonstrated an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8 (standard deviation SD, 0.01), with 51% specificity to reach 85% sensitivity [relative Area Under the Precision Recall Curve (rel-AUPRC) 3.42, SD 0.06] . Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the predictions. In children with no comorbidities admitted between 2009 and 2019, the model achieved a AUROC of 0.77 (SD 0.03) and a rel-AUPRC of 3.31 (SD 0.42) . Conclusion: A machine learning model using data available at time of ICU discharge predicted failure to meet minimum educational requirements at school age. Implementation of this prediction tool could assist in prioritizing patients for follow-up and targeting of rehabilitative measures. Keywords: Child, Intensive care, Machine learning, Neurodevelopment, School  \nDOI: [https://doi.org/10.1007/s00134-023-07137-1](https://doi.org/10.1007/s00134-023-07137-1)  \nPosted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-239397](https://doi.org/10.5167/uzh-239397)  \nJournal Article Published Version  \nThe following work is licensed under a Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4 .0) License.  \nOriginally published at:  \nGilholm, Patricia; Gibbons, Kristen; Brüningk, Sarah; Klatt, Juliane; Vaithianathan, Rhema; Long, Debbie; Millar, Johnny; Tomaszewski, Wojtek; Schlapbach, LuregnJ (2023) . Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study. Intensive Care Medicine, 49(7):785-795.  \nDOI: [https://doi.org/10.1007/s00134-023-07137-1](https://doi.org/10.1007/s00134-023-07137-1)  \nIntensive Care Med (2023) 49:785–795  \n[https://doi.org/10.1007/s00134-023-07137-1](https://doi.org/10.1007/s00134-023-07137-1)  \nORIGINAL  \nMachine learning to predict poor school  \nperformance in paediatric survivors of intensive care: a population-based cohort study  \nPatricia Gilholm 1, Kristen Gibbons 1, Sarah Brüningk2,3, Juliane Klatt2,3, RhemaVaithianathan4, Debbie Long 1,5, Johnny Millar6,7,8, Wojtek Tomaszewski4 and Luregn J. Schlapbach 1,9* on behalf of the Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE) and ANZICS Paediatric Study Group (ANZICS PSG)  \n© 2023 The Author(s)  \nAbstract  \nPurpose: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We dev","cbCaimU1CO1K8YNc","https://ap.wps.com/l/cbCaimU1CO1K8YNc","pdf",282449,1,12,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion\n## Keywords","[{\"question\":\"What school outcome was used to evaluate poor performance?\",\"answer\":\"Poor school performance was defined as failing to meet the National Minimum Standard (NMS) on NAPLAN during primary and secondary school.\"},{\"question\":\"How was the machine learning model trained and validated?\",\"answer\":\"Routine ICU information was used, and models were trained, validated, and tested using stratified nested cross-validation.\"},{\"question\":\"What factors contributed most to the predictions?\",\"answer\":\"Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the model’s predictions.\"}]","Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study | PDF",1785806916,30,{"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},"machine-learning-to-predict-poor-school-performance-in-paediatric-survivors-of-intensive-care-a-population-based-cohort-study","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-to-predict-poor-school-performance-in-paediatric-survivors-of-intensive-care-a-population-based-cohort-study/121796/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What school outcome was used to evaluate poor performance?","Question",{"text":75,"@type":76},"Poor school performance was defined as failing to meet the National Minimum Standard (NMS) on NAPLAN during primary and secondary school.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model trained and validated?",{"text":80,"@type":76},"Routine ICU information was used, and models were trained, validated, and tested using stratified nested cross-validation.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors contributed most to the predictions?",{"text":84,"@type":76},"Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the model’s predictions.","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},[93,97,101,105,110,115,118,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]