[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127927-en":3,"doc-seo-127927-105":31,"detail-sidebar-cat-0-en-105":96},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127927,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Review of machine learning solutions for eating disorders - published version","Eating Disorders (EDs) are complex psychiatric conditions that markedly impair psychological and physical health as well as psychosocial functioning, with low early detection, limited recovery, and high relapse rates. This narrative review summarizes current machine learning and artificial intelligence applications for EDs, emphasizing clinical management, diagnostic and treatment workflows. It compares techniques across multiple use cases and highlights limitations in data quality, model flexibility, explainability, fairness, and trustworthiness.","Review of machine learning solutions for eating disorders  \nCitation for published version (APA):  \nGhosh, S. , Burger, P. , Simeunovic-Ostojic, M. , Maas, J. , & Petković, M. (2024) . Review of machine learning solutions for eating disorders. International Journal of Medical Informatics, 189, Article 105526.  \n[https://doi.org/10.1016/j.ijmedinf.2024.105526](https://doi.org/10.1016/j.ijmedinf.2024.105526)  \nDocument license:  \nCC BY  \nDOI:  \n10.1016/j.ijmedinf.2024.105526  \nDocument status and date:  \nPublished: 01/09/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \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.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 22. Oct. 2024  \nInternational Journal of Medical Informatics 189 (2024) 105526  \nContents lists available at ScienceDirect  \nInternational Journal of Medical Informatics  \njournal [homepage:](homepage: www.elsevier.com/locate/ijmedinf)[ www.elsevier.com/locate/ijmedinf](homepage: www.elsevier.com/locate/ijmedinf)  \n| Review article\u003Cbr>Review of machine learning solutions for eating disorders\u003Cbr>Sreejita Ghosh a,∗, 1 , Pia Burger b,∗, 1 , Mladena Simeunovic-Ostojicb, Joyce Maas b,c, Milan Petkovi´c a,d\u003Cbr>a Dept. M & CS, Technical University of Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, the Netherlands b Center of Eating Disorders, GGZ Oost-Brabant, Wesselmanlaan 25a, 5707 HA Helmond, the Netherlands\u003Cbr>c Dept. Medical and Clinical Psychology, Tilburg University, Prof. Cobbenhagenlaan, 5037 AB Tilburg, the Netherlands d Philips Hospital Patient Monitoring, High Tech Campus 34, 5656 AE Eindhoven, the Netherlands |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Eating disorders Machine learning Causality Actionable healthcare |  | Background: Eating Disorders (EDs) are one of the most complex psychiatric disorders, with significant impairment of psychological and physical health, and psychosocial functioning, and are associated with low rates of early detection, low recovery and high relapse rates. This underscores the need for better diagnostic and treatment methods.\u003Cbr>Objective: This narrative review explores current Machine Learning (ML) and Artificial Intelligence (AI) applications in the domain of EDs, with a specific emphasis on clinical management in t","cbCaih1DPBTKpmG2","https://ap.wps.com/l/cbCaih1DPBTKpmG2","pdf",1589391,4,1,19,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Eating disorders and their health impact\n# Review Scope and Objectives\n# Results and Use Cases\n# Conclusion and Future Directions","[{\"question\":\"What problem do eating disorders cause, according to the review?\",\"answer\":\"Eating disorders significantly impair psychological and physical health and psychosocial functioning, with low early detection, limited recovery, and high relapse rates.\"},{\"question\":\"What does the review aim to do?\",\"answer\":\"It explores current machine learning and AI applications for eating disorders with a focus on clinical management, while highlighting limitations and ways to address them.\"},{\"question\":\"Which clinical use cases for EDs are covered by machine learning methods?\",\"answer\":\"The review includes ED risk factor identification and incidence prediction, social media–based analysis, diagnosis support, patient monitoring, and prediction of treatment response and prognosis.\"},{\"question\":\"What limitations are identified for AI/ML in actionable ED healthcare?\",\"answer\":\"Key limitations include insufficient high-quality and high-quantity data for training, and the need for flexible, high-performing yet explainable models that support counterfactuals, fairness, and trustworthy decisions.\"}]","Review of machine learning solutions for eating disorders - 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