Limitations and future directions
particular disorder, psychological disorder, anxiety
The present studies are not without limitations. Despite the observed large effect sizes for the interaction of cluster membership with treatment response in Study 1In addition, although clusters showed a large confluence of features between both studies (73–98% and between cluster correlations of 0.7–0.9), some distinctions were observed, particularly for the first phenotype on intervention (awareness) related features, and for the second phenotype on outcome/mechanism–related features. It is possible that these differences stem from population differences in baseline anxiety: Study 1 was a treatment-seeking sample with on average moderate levels of anxiety, whereas Study 2 was a larger sample taken from the general population with on average mild levels of anxiety. Moreover, the use of adaptive intervention designsIn conclusion, the results of these studies supported the clinical practical utility of psychological phenotyping using machine learning and self-report questionnaire data involving characteristics related to a psychological treatment’s modality, mechanism of action, and outcome. In other words, machine learning can be used to identify psychological phenotypes, or subgroups of people based on baseline psychological characteristics relevant to a particular disorder that can inform patients’ response to its treatment or psychological disorder susceptibility and have implications in terms of the use of personalized medicine in clinical mental health practice