TY  - GEN
AV  - public
A1  - Schröder-Pfeifer, Paul
Y1  - 2022///
CY  - Heidelberg
TI  - Machine Learning Applications in Psychotherapy Research
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/31214/
ID  - heidok31214
N2  - Prediction of outcome or diagnoses from intake data or assessing the importance of variables as either risk factors or protective factors are fundamental tasks in psychotherapy research, in order to help clinicians and researchers to evaluate and improve treatments. With regard to data analytic assessment, these tasks can be handled by a range of parametric approaches such as regression models. However, there are cases where parametric approaches are either not applicable or have severe limitations (e.g. Strobl et al., 2009). Also, there is increasing support to the notion that biopsychosocial contributions to psychopathology are complex and cannot be sufficiently explained by a small number of variables restricted to linear relationships (Franklin, 2019; Kendler, 2019). Machine Learning (ML) algorithms offer an additional suite of methods able to deal with such complexity and can be used to extend the toolbox of psychotherapy researchers. The aim of the dissertation is to provide an understanding of machine learning application for psychotherapy research and to foster the motivation to use and improve these methods in future research.
ER  -