Related papers: Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning

Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning

URL: http://arxiv.org/abs/2412.20741v1
Date: Mon, 30 Dec 2024 06:33:39 GMT
Title: Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning
Authors: Tomasz Rutowski, Elizabeth Shriberg, Amir Harati, Yang Lu, Piotr Chlebek, Ricardo Oliveira,
Abstract summary: We explore deep language models for detecting depression, anxiety, and their co-occurrence from conversational speech collected during 16k user interactions with an application.<n>We find that results for binary classification range from 0.86 to 0.79 AUC, depending on condition and co-occurrence.
Score: 8.677511646372086
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models for detecting depression, anxiety, and their co-occurrence from conversational speech collected during 16k user interactions with an application. Labels come from PHQ-8 and GAD-7 results also collected by the application. We find that results for binary classification range from 0.86 to 0.79 AUC, depending on condition and co-occurrence. Best performance is achieved when a user has either both or neither condition, and we show that this result is not attributable to data skew. Finally, we find evidence suggesting that underlying word sequence cues may be more salient for depression than for anxiety.

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