A roadmap for applying machine learning when working with privacy-sensitive data - predicting non-response to treatment for eating disorders

Aug 4, 2026

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.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Objectives

Methods

Results

A roadmap for applying machine learning when working with privacy-sensitive data Citation for published version (APA): Svendsen, 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) Document status and date: Published: 14/09/2023 DOI: 10.1080/14737167.2023.2230368 Document Version: Publisher's PDF, also known as Version of record Document license: Taverne Please check the document version of this publication: • 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. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright 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. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If 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: [www.umlib.nl/taverne-license](www.umlib.nl/taverne-license) Take down policy If you believe that this document breaches copyright please contact us at: [repository@maastrichtuniversity.nl](repository@maastrichtuniversity.nl) providing details and we will investigate your claim. Download date: 24 Oct. 2024 Expert Review of Pharmacoeconomics & Outcomes Research ISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/ierp20)[www.tandfonline.com/journals/ierp20](homepage: www.tandfonline.com/journals/ierp20) A road map for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders Vegard G Svendsen, Ben F. M. Wijnen, Jan Alexander De Vos, Ravian Veenstra, Silvia M. A. A. Evers & Joran Lokkerbol To 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 To link to this article: [https://doi.org/10.1080/14737167.2023.2230368](https://doi.org/10.1080/14737167.2023.2230368) Published online: 03 Jul 2023. | Submit your article to this journal | | | --- | --- | | | Article views: 122 | | | View related articles | | View Crossmark data | | | Citing articles: 1 View citing articles | | Full 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) EXPERT REVIEW OF

FAQs

What problem does the paper address?

It addresses how to apply machine learning to clinical data to predict outcomes while preserving privacy when working with patient information.

Which machine-learning methods are used in the study?

The study applies Random Forest and least absolute shrinkage and selection operator to routine outcome monitoring data.

How is the prediction task defined?

It predicts absence of reliable improvement 12 months after entering outpatient treatment for patients with eating disorders.

Aug 4, 2026

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