[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122751-en":3,"doc-seo-122751-105":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},122751,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A roadmap for applying machine learning when working with privacy-sensitive data - predicting non-response to treatment for eating disorders","Objectives focus on how machine-learning methods can be used with clinical data to predict treatment outcomes for psychiatric disorders, while maintaining patient privacy as a central constraint. Methods demonstrate a practical workflow by building a clinically relevant prediction model using two common algorithms, Random Forest and least absolute shrinkage and selection operator, on routine monitoring data from 593 eating-disorder patients. The goal is to predict absence of reliable improvement 12 months after outpatient treatment entry.","A roadmap for applying machine learning when working with privacy-sensitive data  \nCitation for published version (APA):  \nSvendsen, V. G. , Wijnen, B. F. M. , De Vos, J. A. , Veenstra, R. , Evers, S. M. A. A. , & Lokkerbol, J. (2023) . A roadmap for applying machine learning when working with privacy-sensitive data: predicting nonresponse to treatment for eating disorders. Expert Review of Pharmacoeconomics & Outcomes Research, 23(8), 933-949 . [https://doi.org/10.1080/14737167.2023.2230368](https://doi.org/10.1080/14737167.2023.2230368)  \nDocument status and date:  \nPublished: 14/09/2023  \nDOI:  \n10.1080/14737167.2023.2230368  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nDocument license:  \nTaverne  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.umlib.nl/taverne-license](www.umlib.nl/taverne-license)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[repository@maastrichtuniversity.nl](repository@maastrichtuniversity.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 24 Oct. 2024  \nExpert Review of Pharmacoeconomics & Outcomes Research  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/ierp20)[www.tandfonline.com/journals/ierp20](homepage: www.tandfonline.com/journals/ierp20)  \nA road map for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders  \nVegard G Svendsen, Ben F. M. Wijnen, Jan Alexander De Vos, Ravian Veenstra, Silvia M. A. A. Evers & Joran Lokkerbol  \nTo cite this article: Vegard G Svendsen, Ben F. M. Wijnen, Jan Alexander De Vos, Ravian Veenstra, Silvia M. A. A. Evers & Joran Lokkerbol (2023) A road map for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders, Expert Review of Pharmacoeconomics & Outcomes Research, 23:8, 933-949, DOI: 10. 1080/14737167 .2023.2230368  \nTo link to this article: [https://doi.org/10.1080/14737167.2023.2230368](https://doi.org/10.1080/14737167.2023.2230368)  \n Published online: 03 Jul 2023.  \n\n|  Submit your article to this journal  |  |\n| --- | --- |\n|  | Article views: 122 |\n|  | View related articles  |\n|  View Crossmark data |  |\n|  Citing articles: 1 View citing articles  |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=ierp20](https://www.tandfonline.com/action/journalInformation?journalCode=ierp20)  \nEXPERT REVIEW OF","cbCais0rt2uX2vX0","https://ap.wps.com/l/cbCais0rt2uX2vX0","pdf",21128666,1,20,"English","en",105,"# Abstract\n## Objectives\n## Methods\n## Results","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses how to apply machine learning to clinical data to predict outcomes while preserving privacy when working with patient information.\"},{\"question\":\"Which machine-learning methods are used in the study?\",\"answer\":\"The study applies Random Forest and least absolute shrinkage and selection operator to routine outcome monitoring data.\"},{\"question\":\"How is the prediction task defined?\",\"answer\":\"It predicts absence of reliable improvement 12 months after entering outpatient treatment for patients with eating disorders.\"}]","A roadmap for applying machine learning when working with privacy-sensitive data - 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