In Psychotherapy research : journal of the Society for Psychotherapy Research
To determine the relative importance of a wide variety of personality and psychopathology variables in influencing patients' adherence to psychotherapy treatment. Two classification trees were trained to predict patients' (1) treatment utilization (i.e., their likelihood of missing a given appointment) and (2) termination status (i.e., their likelihood of dropping out of therapy prematurely). Each tree was then validated in an external dataset to examine performance accuracy. Patients' social detachment was most influential in predicting their treatment utilization, followed by affective instability and activity/energy levels. Patients' interpersonal warmth was most influential in predicting their termination status, followed by levels of disordered thought and resentment. The overall accuracy rating for the tree for termination status was 71.4%, while the tree for treatment utilization had a 38.7% accuracy rating. Classification trees are a practical tool for clinicians to determine patients at risk of premature termination. More research is needed to develop trees that predict treatment utilization with high accuracy across different types of patients and settings.
Regan Timothy, McCredie Morgan N, Harris Bethany, Clark Shaunna
2023-Mar-07
attendance, classification trees, dropout, machine learning, psychotherapy
