Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts (2020.acl-main)
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| Challenge: | Existing methods to diagnose depression require time-intensive interviews, assessments, and analysis. |
| Approach: | They propose a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. |
| Outcome: | The proposed model outperforms baseline models and provides psycholinguistic insights about depression. |
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| Challenge: | Existing models for depression severity estimations lack uncertainty estimates and temporal interpretability. |
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Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
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