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.

翻译：传统上，自动识别精神障碍的主要方法依赖于使用二元分类器。这些分类器通过访谈场景中获取的行为数据进行训练。在训练过程中，患有特定障碍的个体的数据被归类为正类，而所有其他参与者的数据则构成负类。实践中，人们普遍认识到某些精神障碍具有相似症状，导致收集的行为数据包含与多种障碍相关的多种属性。因此，与目标精神障碍相关的属性也可能存在于负类中。这种数据杂质可能导致针对目标精神障碍的分类器训练效果欠佳。在本研究中，我们以重度抑郁障碍（MDD）和创伤后应激障碍（PTSD）检测为背景探讨这一假设。结果表明，在清除此类数据杂质后，MDD和PTSD的检测性能显著提升。