Shortening and Personalizing Psychodiagnostic Assessments with Decision Tree-Machine Learning Classifiers: An Application Example Based on the Patient Health Questionnaire-9

The development of psychological assessment tools that accurately and efficiently classify individuals as having or not a specific diagnosis is a major challenge for test developers and mental health professionals. This paper shows how machine learning (ML) provides a valuable framework to improve the accuracy and efficiency of psychodiagnostic classifications. The method is illustrated using an empirical example based on the Patient Health Questionnaire-9 (PHQ-9). The results show that, compared to traditional scorings of the PHQ-9, that based on decision tree (DT) algorithms is more advantageous in terms of accuracy and efficiency. In addition, the DT-based method facilitates the development of short test forms and improves the diagnostic performance of the test by integrating external information (e.g., demographic variables) into the scoring process. These findings suggest that DT-algorithms and ML applications such as feature selection represent a valuable method for supporting test developers and mental health professionals, and highlight the potential of ML for advancing the field of psychological assessment.