Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Ayah Zirikly

Abstract:

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 indomain 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.