Analyzing the Effect of Data Impurity on the Detection Performances of Mental Disorders
This addresses a data quality issue for researchers and practitioners in automated mental health diagnostics, but it is incremental as it focuses on refining existing methods rather than introducing a new approach.
The study tackled the problem of data impurity in training binary classifiers for mental disorder detection, where shared symptoms cause attributes of the target disorder to appear in the negative class, and found that removing this impurity significantly improved detection performances for major depressive disorder and post-traumatic stress disorder.
The primary method for identifying mental disorders automatically has traditionally involved using binary classifiers. These classifiers are trained using behavioral data obtained from an interview setup. In this training process, data from individuals with the specific disorder under consideration are categorized as the positive class, while data from all other participants constitute the negative class. In practice, it is widely recognized that certain mental disorders share similar symptoms, causing the collected behavioral data to encompass a variety of attributes associated with multiple disorders. Consequently, attributes linked to the targeted mental disorder might also be present within the negative class. This data impurity may lead to sub-optimal training of the classifier for a mental disorder of interest. In this study, we investigate this hypothesis in the context of major depressive disorder (MDD) and post-traumatic stress disorder detection (PTSD). The results show that upon removal of such data impurity, MDD and PTSD detection performances are significantly improved.