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Verfasst von:Rollmann, Ivo [VerfasserIn]   i
 Gebhardt, Nadja [VerfasserIn]   i
 Stahl-Toyota, Sophia [VerfasserIn]   i
 Simon, Joe J. [VerfasserIn]   i
 Sutcliffe, Molly [VerfasserIn]   i
 Friederich, Hans-Christoph [VerfasserIn]   i
 Nikendei, Christoph [VerfasserIn]   i
Titel:Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research
Verf.angabe:Ivo Rollmann, Nadja Gebhardt, Sophia Stahl-Toyota, Joe Simon, Molly Sutcliffe, Hans-Christoph Friederich and Christoph Nikendei
E-Jahr:2023
Jahr:09 May 2023
Umfang:8 S.
Fussnoten:Gesehen am 27.06.2023
Titel Quelle:Enthalten in: Frontiers in psychiatry
Ort Quelle:Lausanne : Frontiers Research Foundation, 2007
Jahr Quelle:2023
Band/Heft Quelle:14(2023), Artikel-ID 1055868, Seite 1-8
ISSN Quelle:1664-0640
Abstract:Introduction: Although outpatient psychodynamic psychotherapy is effective, 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.MethodsFor this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.Results: 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.Discussion: 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.
DOI:doi:10.3389/fpsyt.2023.1055868
URL:kostenfrei: Volltext: https://doi.org/10.3389/fpsyt.2023.1055868
 kostenfrei: Volltext: https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1055868
 DOI: https://doi.org/10.3389/fpsyt.2023.1055868
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1851056688
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