Automated methods have been widely used to identify and analyze mental health
conditions (e.g., depression) from various sources of information, including
social media. Yet, deployment of such models in real-world healthcare
applications faces challenges including poor out-of-domain generalization and
lack of trust in black box models. In this work, we propose approaches for
depression detection that are constrained to different degrees by the presence
of symptoms described in PHQ9, a questionnaire used by clinicians in the
depression screening process. In dataset-transfer experiments on three social
media datasets, we find that grounding the model in PHQ9's symptoms
substantially improves its ability to generalize to out-of-distribution data
compared to a standard BERT-based approach. Furthermore, this approach can
still perform competitively on in-domain data. These results and our
qualitative analyses suggest that grounding model predictions in
clinically-relevant symptoms can improve generalizability while producing a
model that is easier to inspect.

本研究提出通过将模型约束在医生在抑郁筛选过程中使用的 PHQ9 表中描述的症状中以不同程度地控制抑郁症检测的方法。在三个社交媒体数据集的数据集转移实验中，我们发现将模型限定于 PHQ9 的症状可以显著提高模型对于分布外数据的泛化能力，而在本领域数据上也可以具有竞争力。此外，这种方法还可以产生更易于检查的模型，从而提高了模型的泛化能力。