[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117795-en":3,"doc-seo-117795-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117795,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research - Mini Review","Outpatient psychodynamic psychotherapy is effective for multiple psychological disorders, yet recent years have seen limited improvement in overall treatment success. This systematic review evaluates how machine learning is currently used in outpatient psychodynamic psychotherapy research, focusing on statistical methods that aim to predict individual outcomes such as dropout. Using the PRISMA guidelines, the review identifies four relevant studies and discusses emerging perspectives to support new, more personalized approaches to address previously unsolved clinical problems.","TYPE Mini Review  \nPUBLISHED 09 May 2023  \nDOI 10. 3389/fpsyt.2023.1055868  \nOPEN ACCESS  \nEDITED BY  \nAndreas Stengel,  \nUniversity Hospital Tübingen, Germany  \nREVIEWED BY  \nDavid Benrimoh,  \nMcGill University, Canada  \n*CORRESPONDENCE  \nIvo Rollmann  \n ivo. rollmann@med. uni-heidelberg.de  \nRECEIVED 28 September 2022  \nACCEPTED 17 April 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nRollmann I, Gebhardt N, Stahl-Toyota S, Simon J, Sutcli􀀀e M, Friederich H-C and Nikendei C (2023) Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research.  \nFront. Psychiatry 14:1055868 .  \ndoi: 10.3389/fpsyt.2023.1055868  \nCOPYRIGHT  \n© 2023 Rollmann, Gebhardt, Stahl-Toyota, Simon, Sutcli􀀀e, Friederich and Nikendei. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nSystematic review of machine learning utilization within outpatient psychodynamic psychotherapy research  \nIvo Rollmann*, Nadja Gebhardt, Sophia Stahl-Toyota, Joe Simon, Molly Sutcli􀀀e, Hans-Christoph Friederich and  \nChristoph Nikendei  \nDepartment for General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany  \nIntroduction: Although outpatient psychodynamic psychotherapy is e􀀀ective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient’s needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives.  \nMethods: For this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.  \nResults: In total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021 .  \nDiscussion: We conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems.  \nKEYWORDS  \nmachine learning (ML), psychodynamic psychotherapy, outpatient therapy, review—systematic, perspectives  \nIntroduction  \nOutpatient psychodynamic psychotherapy is e􀀓ective in treating various psychological disorders (1–3) . Further positive e􀀓ects include a reduced number of sick leaves, a reduction of health care utilization, less psychiatric hospitalizations after therapy, and a reduced relapse rates for depression (4–6) . A number of factors that predict successful therapy are also known, such as improving the working alliance (7, 8), therapeutic agency (7, 9, 10) orthe patient’s ability to perceive emotions (11, 12) which lead to a reduction in symptom burden. However, as Leichsenring et al. (13) point out, recent substantial improvements in  \nFrontiersin Psychiatry 01 [frontiersin.org](frontiersin.org)  \ntreatment success have been scarce. The authors (13) recommend that future studies focus primarily on non-responders and dropouts ","cbCaib6JT5KZPy31","https://ap.wps.com/l/cbCaib6JT5KZPy31","pdf",205649,1,"English","en",105,"# Introduction\n## Machine learning in psychotherapy research\n# Methods\n## PRISMA-based systematic review\n# Results\n## Included studies and publication window\n# Discussion\n## Implications and future perspectives","[{\"question\":\"What problem does the review address in outpatient psychodynamic psychotherapy?\",\"answer\":\"Treatment success has not improved substantially in recent years, despite the effectiveness of outpatient psychodynamic psychotherapy for several psychological disorders.\"},{\"question\":\"How were studies selected for the systematic review?\",\"answer\":\"The review applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses (PRISMA) guidelines to guide the systematic review process.\"},{\"question\":\"What did the review find about the use of machine learning so far?\",\"answer\":\"Only four studies using machine learning in outpatient psychodynamic psychotherapy research were identified, with three published between 2019 and 2021.\"}]","Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research - 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