  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.
