[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128087-en":3,"doc-seo-128087-105":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},128087,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands - development and external validation of machine learning models","Large registries offer strong opportunities for studying inflammatory bowel disease epidemiology. This study developed and validated machine learning models to identify IBD cases in administrative data, with the goal of estimating prevalence, incidence, and mortality in the Netherlands. Models were trained in a population-based cohort and externally validated in a hospital cohort, using Brier score, AUC, calibration, and accuracy. Best-performing models were applied to 2013–2020 data, revealing high case identification performance and increasing mortality alongside stable incidence trends.","EUR Research Information Portal  \nPrevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands  \nPublished in:  \nJournal of Crohn's & colitis  \nPublication status and date:  \nPublished: 04/02/2025  \nDOI (link to publisher):  \n10.1093/ecco-jcc/jjaf017  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument License/Available under:  \nCC BY-NC  \nCitation for the published version (APA):  \nvan Linschoten, R. C. A. , van Leeuwen, N. , van Klaveren, D. , Pierik, M. J. , Creemers, R. , Hendrix, E. M. B. , Hazelzet, J. A. , van der Woude, C. J. , West, R. L. , & van Noord, D. (2025) . Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models. Journal of Crohn's & colitis, 19(2), Article jjaf017 . [https://doi.org/10.1093/ecco-jcc/jjaf017](https://doi.org/10.1093/ecco-jcc/jjaf017)  \nLink to publication on the EUR Research Information Portal  \nTerms and Conditions of Use  \nExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third party without the prior written permission from the copyright holder(s) . Copyright law allows the following uses of this material without prior permission:  \n• you may download, save and print a copy of this material for your personal use only;  \n• you may share the EUR portal link to this material.  \nIn case the material is published with an open access license (e.g. a Creative Commons (CC) license), other uses may be allowed. Please check the terms and conditions of the specific license.  \nTake-down policy  \nIf you believe that this material infringes your copyright and/or any other intellectual property rights, you may request its removal by contacting us at the following email address: [openaccess.library@eur.nl. Please](openaccess.library@eur.nl. Please) provide us with all the relevant information, including the reasons why you believe any of your rights have been infringed. In case of a legitimate complaint, we will make the material inaccessible and/or remove it from the website.  \nPrevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands: development and external validation of machine learning models  \nReinier C.A. van Linschoten1,2,3,*,, Nikki van Leeuwen3, David van Klaveren3, Marieke J. Pierik4,5,, Rob Creemers5,6,, Evelien M. B. Hendrix4,5,, Jan A. Hazelzet3,, C. Janneke van der Woude2, Rachel L. West1,\\#, Desirée van Noord1,\\#,  \n1Department of Gastroenterology & Hepatology, Franciscus Gasthuis & Vlietland, P. O. Box 10900, 3004 BA, Rotterdam, The Netherlands 2Department of Gastroenterology & Hepatology, Erasmus MC, P. O. Box 2040, 3000 CA, Rotterdam, The Netherlands  \n3Department of Public Health, Erasmus MC, P. O. Box 2040, 3000 CA, Rotterdam, The Netherlands  \n4Department of Gastroenterology and Hepatology, MUMC+, P. O. Box 5800, 6202 AZ, Maastricht, The Netherlands  \n5 NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, P. O. Box 616, 6200 MD, Maastricht, The Netherlands  \n6Department of Gastroenterology, Geriatrics, Internal and Intensive Care Medicine (Co-MIK), Sittard-Geleen, Zuyderland Medical Centre, P. O. Box 5, 6130 AA, Heerlen, The Netherlands  \n* Corresponding author: Reinier C.A. van Linschoten, Franciscus Gasthuis & Vlietland, P. O. Box 10900, 3004 BA Rotterdam, The Netherlands (r.vanlinschoten@ [erasmusmc.nl](erasmusmc.nl)) .  \n\\#Rachel L. West and Desirée van Noord share last authorship.  \nAbstract  \nBackground and aims: Large registries are promising tools to study the epidemiology of inflammatory bowel disease (IBD) . We aimed to develop and validate machine learning models to identify IBD cases in administrative data, aiming to determine the prevalence, incidence, and mortality of IBD in the Netherlands.  \nMethods: We developed machine learning models for administ","cbCaibWDba7Yv4HK","https://ap.wps.com/l/cbCaibWDba7Yv4HK","pdf",715115,1,12,"English","en",105,"# Abstract\n## Background and aims\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Epidemiological stages of IBD in the Netherlands\n# Methods and validation approach\n## Model development and external validation\n## Evaluation metrics\n# Findings\n## Prevalence, incidence, and mortality results\n# Conclusion and implications","[{\"question\":\"What was the main goal of this study?\",\"answer\":\"To develop and externally validate machine learning models that identify inflammatory bowel disease cases in administrative data, enabling estimation of prevalence, incidence, and mortality in the Netherlands.\"},{\"question\":\"How were the machine learning models evaluated?\",\"answer\":\"Models were assessed using Brier score, area under the ROC curve (AUC), calibration, and accuracy, and then used to derive epidemiological estimates for 2013–2020.\"},{\"question\":\"Which models performed best for key tasks?\",\"answer\":\"Random forest performed best for identifying IBD cases, gradient-boosted trees performed best for subtype classification, and random forest performed best for incidence year.\"}]","Prevalence, incidence, and mortality of inflammatory bowel disease in the Netherlands - development and external validation of machine learning models | PDF",1785944731,30,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"prevalence-incidence-and-mortality-of-inflammatory-bowel-disease-in-the-netherlands-development-and-external-validation-of-machine-learning-models","",{"@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/prevalence-incidence-and-mortality-of-inflammatory-bowel-disease-in-the-netherlands-development-and-external-validation-of-machine-learning-models/128087/",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","2026-08-05",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 and externally validate machine learning models that identify inflammatory bowel disease cases in administrative data, enabling estimation of prevalence, incidence, and mortality in the Netherlands.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the machine learning models evaluated?",{"text":81,"@type":77},"Models were assessed using Brier score, area under the ROC curve (AUC), calibration, and accuracy, and then used to derive epidemiological estimates for 2013–2020.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models performed best for key tasks?",{"text":85,"@type":77},"Random forest performed best for identifying IBD cases, gradient-boosted trees performed best for subtype classification, and random forest performed best for incidence year.","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},[94,98,102,106,111,116,121,123,128,131,135],{"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":29,"slug":122},"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]